academic.oup.com/bjs/article-lookup/doi/10.1093/bjs/znae097
Patients with female doctors have a lower risk of death or serious complications, research shows | The study compared hospitals in Canada where female doctors made up more than 35% of the surgical teams with a 3% lower chance of serious health complications for patients.
Social ScienceI think this is confounded by age effects, which was not examined in the study. Older doctors sometimes do worse (Tsugawa et al, 2017), and there are more older male doctors than older female doctors (not cited).
Also, their definition of higher sex diversity is >35% female, but I guess they couldn't find any 100% female teams to actually look at sex diversity.
It is also confounded by the reality that more challenging and risky operations will go to the most experienced (i.e "old" surgeons)
Another recent study showed that female patients have better outcomes with female surgeons. In that study, they noted that female surgeons tended to be younger and have lower annual surgical volumes than male surgeons, and that they treated younger patients with less comorbidity than male surgeons.
And that study team conducted analyses to determine whether these kinds of variables were driving the differences. They stratified several relevant variables (surgeon age, surgery volume, surgeon specialty, years in practice, patient age, patient comorbidity index, etc) and assessed the impact of sex concordance/discordance within those strata. For example, they looked at the impact of sex concordance/discordance for male surgeons with <1-5 years of experience compared females with <1-5 years of experience. And for male surgeons with 6-10 years of experience compared with females with 6-10 years of experience. And for male surgeons with 15+ years of experience compared with female surgeons with 15+ years of experience, etc. And almost all of the comparisons showed the same result - female patients had better outcomes with female surgeons. Some exceptions were comparisons of female vs male surgeons in urology and in vascular surgery, and those in academic hospitals), for which there was no statistically significant difference. Notably, for every comorbidity index female patients had better outcomes with female surgeons.
This study didn’t look at years of experience, but it did assess the impact of all the other variables mentioned above.
Sorry, I can't find them correcting for years of experience in the original article.
The following covariates were adjusted for: patient age, sex, and comorbidity burden, annual hospital, surgeon and anesthesiologist volumes, type of surgery, and year of surgery.
The term they use is "year of surgery" which refers to the year the surgery takes place, not the years of experience of the doctors.
the_real_dairy_queen is talking of a different study, with a different research question...
Ah, then I was confused.
I see the issue, Tsugawa et al. 2017 refers to multiple papers, both relevant to the conversation. I was referring to "Physician age and outcomes in elderly patients in hospital in the US", they are probably referring to "Comparison of Hospital Mortality and Readmission Rates for Medicare Patients Treated by Male vs Female Physicians".
You’re right - I glanced and assumed this was a repost of this JAMA article posted on 5/6. I’ve edited my post to reflect this.
So it’s true that this paper doesn’t assess the impact of years of experience, but it assesses the other variables I mentioned. And it’s notable that a similar study showed that female physicians had better outcomes regardless of years of experience.
No worries. Reddit's a mess, and the UI could be far better.
I wouldn't put so much trust in the results of this study for reasons I have stated elsewhere. I'm happy to summarise them if you're interested, but I don't want to spam you if you have already seen them.
I haven't examined the other study, so I can't really offer any comment. I do wonder, however, if the association shown is uniformly distributed across the years or if it is only seen recently. My (anecdotal) observation is that there's a growing mistrust of women towards men. Perhaps at least part of the effect that we're seeing is the result of "more stable" female patients, with better baseline prognosis, having the time to look around for a female surgeon, while more "critically ill" patients (and thus worse baseline prognosis) can't. Given that surgery is still a male-dominated discipline, that would mean male surgeons would get a higher proportion of less stable patients. Commonly used comorbidity scores don't really capture the more immediate physiological state of the patients. Is there any study that you know that adjusts for the preoperative state?
In academic hospitals (from my experience), the patient has less control regarding their surgeon, and thus, patients get more evenly split, and we fail to detect an association.
Once again, these are my thoughts and not criticisms of the other paper. I'd greatly appreciate your opinions if you found my ramblings at least a bit amusing.
It’s also possible that to succeed as a surgeon in a very male-dominated field, women have to significantly outperform their peers.
True, it's possible. The implication is some survivorship bias, where a very small effect size is only driven by a selected group of female surgeons not necessarily representative of the rest... That's problematic.
You’re right - I glanced and assumed this was a repost of this JAMA article posted on 5/6. I’ve edited my post to reflect this.
So it’s true that this paper doesn’t assess the impact of years of experience, but it assesses the other variables I mentioned. And it’s notable that a similar study showed that female physicians had better outcomes regardless of years of experience.
I’m a little confused by the way they analyze the data and present the results. For example:
Further analyses support that worse outcomes among female patients treated by male surgeons drives the observed association of sex discordance.
But isn’t that a premature conclusion…how do we know it’s not the other way around, that we’re just seeing better outcomes among female patients treated by female surgeons? Those would present the same mathematically, but the actual causal mechanism could be completely different.
And lo and behold, in the article:
While male patients consistently had higher rates of postoperative events (eFigure in the Supplement), there were relatively small differences in rates of composite adverse postoperative outcomes among male patients treated by male and female surgeons (range in difference between male and female surgeons, 0.1% to 0.4% among specialties), while female patients treated by male surgeons had consistently higher adjusted rates of postoperative events compared with those treated by female surgeons (range in difference between male and female surgeons, 0.6% to 2.5% among specialties) (Table 2).
So women see better results under both genders, it’s just betterer under women?
Of course, that would be a premature conclusion too, since gender is confounded with behavior and physical health, so there is no apples to apples comparison.
And then:
The association of sex discordance was limited to female patients treated by male surgeons compared with female patients treated by female surgeons (composite end point: aOR, 1.15; 95% CI, 1.10-1.20) and was not found among male patients treated by female surgeons compared with male patients treated by male surgeons (composite end point: aOR, 0.99; 95% CI, 0.95-1.03) (P for interaction = .004). A similar pattern emerged for each end point: outcomes for discordant female surgeon/male patient dyads were comparable or better than those of the male surgeon/male patient dyads, while discordant male surgeon/female patient dyads had consistently statistically significantly worse outcomes than female surgeon/female patient dyads (Table 3). As with the first binary operationalization of sex discordance, we performed stratified subgroup analyses according to surgeon, patient, procedural, and hospital, again with the cohort stratified according to patient sex. Within each group, we examined the association between male and female surgeons and the primary composite adverse postoperative outcome, for each subgroup. While we found consistent evidence of comparable or somewhat better outcomes for male patients treated by female surgeons, this association was significantly larger for female patients and consistent across subgroups (Figure 2).
So we don’t see men have different results but we do see women have different results. And here is where I’m struggling with the way the data is accounted for:
How do you adjust for risk in surgery for gender without also confounding sexism? If men have worse results in surgery everywhere because of pervasive sexism that both male and female surgeons hold, wouldn’t the data prevent the exact same? With no change in male/male versus female/male because men don’t have an in-group bias, and a big change in male/female versus female/female because women do have an in-group bias? How do you tease out those two results?
What are some examples of pervasive sexism against men that could account for these results?
I know women often feel like male doctors don’t treat their problems seriously. So I could imagine something like women not feeling comfortable speaking up after surgery to a male surgeon if something seems wrong. Do you think men are less likely to speak up to a male surgeon because of the stigma around seeming “weak”? Or is there another potential effect you are considering?
I was thinking of the Women are Wonderful effect, specifically at
This research found that while both women and men have more favorable views of women, women's in-group biases were 4.5 times stronger[5] than those of men. And only women (not men) showed cognitive balance among in-group bias, identity, and self-esteem, revealing that men lack a mechanism that bolsters automatic preference for their own gender.[5]
Given that we see the same breakdown of bias here—where the bias is strong for women but weak for men—it could be a similar mechanism. In which case it would be that male surgeons care more about the well-being of their female patients over their male patients—but female surgeons have a stronger bias.
That’s really interesting, and compelling. I would think that females would do better under males AND females based on that effect. But, then, it’s possible it’s two separate effects - for example the Women are Wonderful effect to explain better outcomes for women under women, and maybe the phenomenon where women feel like a male surgeon won’t take them seriously to explain why women don’t also do better under men. Maybe the “Women are wonderful” effect is true for male and female surgeons, but women’s negative perception of male physicians cancels out the “women are wonderful” effect?
I would think that females would do better under males AND females based on that effect.
The study does show that females do better under both males and females:
While male patients consistently had higher rates of postoperative events (eFigure in the Supplement), there were relatively small differences in rates of composite adverse postoperative outcomes among male patients treated by male and female surgeons (range in difference between male and female surgeons, 0.1% to 0.4% among specialties), while female patients treated by male surgeons had consistently higher adjusted rates of postoperative events compared with those treated by female surgeons (range in difference between male and female surgeons, 0.6% to 2.5% among specialties) (Table 2).
That's what i quoted.
As far as I can tell, the reason that it's not detailed in the data is because the fact that men simply have worse outcomes is already controlled for. But by controlling for sex, you also control for sexism--so if men's results are worse because of sexism from both men and women, it would just appear as men having worse outcomes.
Of course, if men do just have worse outcomes with the same treatment, the data would also look the same there.
That's why I was confused about the causative statement to begin with--there is no neutral baseline to be better or worse than because each gender has their own baseline based on treatment from both genders. So I don't understand how you can untangle "women do worse under male surgeons" versus "women do better under female surgeons".
“Higher rates of postoperative events” is a worse outcome. “Postoperative events” are complications.
Yeah, and men had worse outcomes:
While male patients consistently had higher rates of postoperative events
Pretty sure it’s backed up in the figures and data as well.
I think you may be in the wrong thread. Are you not looking for this one instead?
However...
But isn’t that a premature conclusion…how do we know it’s not the other way around, that we’re just seeing better outcomes among female patients treated by female surgeons?
I think here you're conflating "worse" with bad and "better" with good. "Worse" and "better" are relative terms. I could win 1,000,000 quid (which would be very good) and be worse than someone who won 2,000,000.
The choice depends on which group (i.e. Men/Women) you're using as a reference. That being said, some authors may choose words for their connotations or to obscure details. One particular nasty example was Rofecoxib, where the authors claimed that naproxen (the reference group) protected against heart attacks. In reality, rofecoxib increased the risk... so some food for thought.
I’m pretty sure that this is the right thread?
And yeah, that’s my entire point. The way this study is reported, it is natural to look for ways that male surgeons discriminate against women, rather than a more even-handed approach that might ask more about why men are doing worse than women across the board. So you could end up exacerbating equality, rather than addressing it.
I think you may mean inequality... unless you're very strongly anti-communist.
I agree with your point, though.
EDIT: Sorry, mate. My bad. I thought you were posting in the general thread, but I now realise you were replying to the_real_dairy_queen.
Males also tend to take more risks in general. We know men are more likely to work midnight ER shifts or be surgeons, while family medicine and pediatrics tend to draw more women. I wouldn't be surprised if these same trends held up within each field, with more men willing to perform risky procedures, or more willing to operate on patients with more risk factors for complications.
Did they not control for age in the study?
I think they refer to the surgeon's age. The patient's age was included as a covariate in the model.
Also confounded by ridiculous levels of publishing bias. If the results said the opposite, the manuscript wouldn’t have seen the light of day.
There are so many people trying to write DEI compatible papers because they get lots of attention.
So hundreds of researchers are checking hundreds of databases for tidy PC manuscripts. So every paper says minority/ women docs have mortality benefit
The more I look at the paper, the more concerns I have about the transparency of their methods.
Also, similar papers have p values close to 0.04, which seems a bit odd given their large sample sizes.
Also, similar papers have p values close to 0.04, which seems a bit odd given their large sample sizes.
And the relative ease of making similar studies. It's "just" a statistical analysis, on ore collected, and once you make one you can track it over hundreds of variables, sex, gender, race, immigration status, school, etc.
Which makes it super easy to p-fix studies like this. I'm not saying these people did it, but it's quite easy to do it on a systemic level (and by that I mean the research field as a whole) by accident.
Given that hundreds of researchers might be doing similar work and finding nothing, it just so happened that they here hit the 5% the p requires.
The current situation where we evaluate p-values on a single paper basis is a massive issue, leading to random and unusable noise in our research data.
Stuff like this is why I have a lot less faith in academia these days. Learning how to critically assess methodology is immensely important, but not easy to teach and not easy to learn. It took me quite a while to grasp the concepts of my Assessing Research module back in the day.
The future of science lies in the R&D of companies and not in academia. Or at least the peer-reviewing & publishing phase.
Do you mean pharmaceutical companies or independent companies with no conflict of interest?
Does this also take into consideration how many hours are worked by each group? Not saying men work more hours, and not saying that women’s disproportionate home life contributions don’t equally contribute to their fatigue levels, but I can probably think of many variables that would be interesting to further investigate
Not as bad as the earlier paper on this, in that they are least attempted to adjust for a reasonable set of confounders but come on: after adjustment the odds ratio was .97, meaning a whopping 3% relative difference in morbidity, and just baaaarely significant. There is a lot of scope to the analysis design to p-hack to a significant result. Even if they didn't, what are the odds that the effect has anything to do with team diversity or provider sex vs just residual confounding?
Eyeing the results, patients in the high diversity group seem to be skewed towards less economic deprivation, whereas we see a higher representation of the more deprived group in the less diversity group. This wasn't adjusted for, but what I really wonder if it is that "nicer" hospitals (i.e. with more resources) are confounding the results.
Odds ratios also distort the actual risk, unless you're talking of very rare events. The actual effect is likely smaller.
Could also be emergency vs elective, low socioeconomic groups tend toward that.
Excellent point! Also, I'm not sure in Canada, or whether it is captured with the scale they're using, but from experience people from more deprived areas would have comorbid states that are not captured by the Elixhauser score.
EDIT: Only elective procedures were included. However, I wonder if patients in more deprived areas were on the waiting list for longer.
Also the type of surgery, as another poster pointed out there is a distinct preference for certain specialties for women, so you could have a comparison between very different surgeries being made.
They accounted for medical specialities but didn't provide much information on specific procedures, some of which can have different risk profiles. I'm also not sure how exactly they choose which procedures to include. They state: "Commonly performed procedures associated with higher morbidity risk were included given the higher potential for association with perioperative team diversity". That seems a bit vague to me, but who knows.
By design I suspect given the low difference.
Is 3% statistically significant?
Statistical significance and clinical significance/effect size are two different concepts.
Statistical significance tells you how likely it is that the pattern you're observing is due to random chance, given the data that you have.
Effect size is how strong the pattern you're observing is. Clinical significance is your judgement on whether the effect you're observing is enough to make a difference in patient care.
You can have statistically significant results that are not clinically significant. You can have results that have a massive effect size, but you can't conclude they weren't due to random chance alone.
Your p-value tells you how likely it is that your findings were observed due to random chance, and your Odds ratios/relative risk/ risk reduction/etc. tell you how strong your findings were. You ideally want strong effects with a low chance of being observed by random chance.
(I'm simplifying things a bit, it's a bit more nuanced, but for practical purposes that's about it)
Thank you.
So, is 3% either statistically significant or clinically significant?
It's statistically significant because the p-value was 0.02*. whether it is clinically significant... It's harder to say... The measure they used (odds ratios) is tricky to translate into actual risks... But to give you an idea, a similar study estimated the difference in risks to be at around 0.5%... which means for every 200 patients, one would benefit.
*I question if we can actually interpret the p-value of 0.02 as statistically significant because the authors did something known as data snooping... But in general, this p-value would be statistical significant
So the odds that a 3% difference was random were only 2% given a p-value of 0.02, right? How do you calculate the p-value?
Yes. Technically it would be more like "the probability of observing a difference of at least 3% due to random chance alone is 2% given a p value of 0.02
For the p-values, I would recommend watching statquest's video on YouTube... It's a far better explanation than what I could type on my phone at the moment... It's in general a great resource if you want to learn about statistics. Hope that helps:)
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17d
"Not as bad as the earlier paper on this[...]"
I don't want to imagine how bad the previous paper was... This is an atrocious piece of work. The more you look at it, the worse it gets. Shady methodology with some questionable choices never explained, and the authors don't even seem that interested in discussing their results, just expressing conclusions that seem to have been reached a priori.
Wasn’t there a study on how if females patients have female doctors, it can lead to better outcomes?
Yes, there have been a lot of studies that have found consistent but small benefits of being treated by female doctors vs male doctors, this one was posted here recently: https://www.reddit.com/r/science/comments/1cam6rm/women_are_less_likely_to_die_when_treated_by/
Right, that was the even worse study I referenced above.
More like consistently and suspiciously small. I suspect you'd see exactly as many publications finding the inverse result, if those were appropriate to publish.
so true. the secret cabal of women that control everything wouldnt allow it. after all, men are oppressed and controlled globally by women and generally can do no wrong
It’s happened before:
https://www.psychologytoday.com/us/blog/checkpoints/202101/the-bad-retraction
If journals only publish results that make women look good but are forced to retract results that make men look good, we’ll see more results like this.
And it’s weird that you think accusations of sexism should just be dismissed out of hand. Do you also mock women for thinking there is some secret cabal of men directing all oppression?
yes, actually. people like me are regularly targeted by "feminists" who espouse views like that. i find them very stupid.
i'm going to move for now, as you've already started claiming i hold views i have not expressed and we're not even one reply in.
Consistently significant but large p-values (around 0.04) are suggestive of p-hacking or selective reporting. You can look up "p-curves" if you're interested.
Their p-values were quite small though. Not suggestive of p hacking. And a p value of .04 does not immediately suggest p hacking
This study, the p-value was 0.02, other studies have been around of 0.04... I have only seen one study with p value of less than 0.001, but I have not systematically searched the literature.
One p value of 0.04 doesn't suggest anything, but pvalues for a given research question consistently around 0.04 can suggest p value hacking or selective reporting as you would expect more pvalues below 0.01 if the alternative hypothesis was indeed correct.
To be clear, I'm not claiming p hacking, but looking at some papers on this subject, I do think it's sensible to examine the possibility
But p value in and of itself doesn’t suggest anything. It has to do with the power as well. What other studies are you referring to?
Mmm have a look at p-curves... All numbers have a distribution. If there is a real effect, you would expect to see a less skewed distribution.
Not one p value, but several from other similar studies.
I'm looking at the studies cited on the authors discussion.
Once again, I'm not claiming anything here because I haven't looked at the topic in depth, those are my initial impressions
Yes, p curves are a thing, but they balance power and p values. And p hacking is evaluated within the context of one study (case by case), you don’t look at research conducted by other authors as evidence of p hacking by other unrelated authors.
However, this study does have evidence of p-hacking. The authors seem to have used the same data to construct their hypothesis, specifically to define their exposed and non-exposed groups. The authors used the relationship between major morbidity and the percentage of female surgeons and anesthesiologists to determine the cut-off point as stated in the paper:
"Restricted cubic splines demonstrated an inflection point at 35% (Fig. S2). This was chosen as the cut-off point[...]"
Since major morbidity and mortality are tightly correlated, the authors are testing for a pattern they already identified in the data and presenting it as their primary finding.
While I understand critical mass is an important concept, as per the article's discussion, the correct methodology would've been defining the cut-off point a priori, based on other studies, or using a random sample from their full data to determine the cut-off point, "discard" this sample, and test their hypothesis on the unused data set. There's no evidence the authors did the latter.
In terms of p-hacking concerns, the exact threshold is relatively minor. Top concerns for me would be: the choice to binarize at all, which is not actually a natural choice for this data; which confounders to include; how conditions are grouped: and which surgical teams were included.
But really, it's not necessary to get into the specific ways the authors might have engineered a significant result. It has a bunch of red flags for unreliability:
- no randomization, not even natural
- tiny effect
- p near cutoff, despite a ton of data
- no preregistration of analysis
- two populations compared are different in many relevant variables
EDIT ---------------------------------------------------------------------------------
Upon further reflection, I do find it concerning that people underestimate the gravity of data snooping.
If you have a set of data, look at it, find a pattern and then test the same set of data for that pattern, a significant result is meaningless. You don't know if that pattern is due to a true signal or just noise. You'd need to use a different set of data to truly test if the pattern is due to a true signal and not a quirk on the data where you initially observe it.
I know that it may seem minor because it's sadly common for some researchers to do this, but it invalidates any potential conclusions you may draw from the results.
I'm not saying it is the only concern, but it's important context to interpret the study. Particularly for two of your points:
Tiny effect and p value near of cut off... The authors visualised the data, choose the cut off point that best benefited them (35 , when values after I think 40 are outliers) and still only managed to get a small effect size...
The p value cannot even be interpreted as significant because of the data snooping.
Additionally, not related to p-hacking but to one of your points:
No preregistration of analysis. The supplementary material seems to indicate more covariates were planned for inclusion into the model (presumably based on the DAG the authors build) but they are dropped without explanation, and the DAG is nowhere to be seen. I'm particularly interested in socioeconomic status, because it differs significantly between groups, is a variable they had and was in the pre-specified covariates... But it's not in the final model... No explanation... A bit suspicious to say the least.
They used a statistical analysis (restricted cubic splines) to identify the cutoff point of what value to use to represent “sex diversity” (which they used to then categorize groups as gender “diverse” or “non diverse”). it wasn’t done arbitrarily but statistically. They then found a relationship between sex diversity and comorbidity. I’m not sure I understand your point about comorbidity and mortality. They didn’t use those criteria to separate the groups, they separated them by gender diversity (statistically, not arbitrarily).
You both misunderstood the paper and my point.
They used splines to smooth the rate of major morbidity, but choose the the cut off point visually.
The issue is that they defined the groups they are comparing using the same data they analysed. This is the proverbial "Painting targets around the arrows".
No, they used splines to identify which groups were gender diverse and which weren’t: “Restricted cubic splines were used to identify a clinically meaningful dichotomization of team sex diversity.”
It’s disingenuous to pretend that’s what they’re saying.
It’s quite obvious that nobody is going to want to publish a study finding that male led teams have better outcomes. That’s not because of some cabal. It’s because of general societal attitudes. Do you actually disagree with that?
i absolutely disagree with that
You know ppl have to fund these studies tho right?
yeah, and they do. they funded the study this post is about not knowing if the outcome would lean towards men or women. how do you think tje process of getting funding works?
Anecdotally, but the head of the department of a place I used to work (well established university, decent reputation) literally pushed us to try and report something favourable to the views of the sponsor (a non profit) when we mentioned the results were negative.
The grant may be given before the results, but future grants may be jeopardised if you don't align with the mission and the vision of the funding body. It's their money and it's understandable (and morally questionable) if they want people who will further their cause.
That's why critical appraisal is so important, not only the methods but also conflicts of interest.
Do you not think that the grant proposal wouldn't have touted the importance of investigating the benefits of diversity?
I'm pro diversity generally, given that I'm not white, but come on.
Come on to what? I said that studies have bias, thats something you learn about studies and research in school. You learn to pick apart research and discuss things. Whether it's the methods they use or the samples they use for the study, whatever.
Nothing I said was anti diversity. So what does that have to do with anything? Why do ppl on the net just jump to these things like moths to a flame?
You disagree that studies have a bias therefore I'm against diversity? Like what point or argument are you attempting to make? It makes no sense
Not true. If there was evidence indicating men outperformed women as surgeons, the field would respond in a way consistent with general societal attitudes, but not in the way you think. They’d immediately begin doing research trying to unpack why, offering explanations, creating gender specific training, etc. for instance, there is a well known finding that certain minorities perform worse on standardized tests, impacting school acceptance and job opportunities. There is a corresponding focus in related research trying to address this problem (alternate tests, for example)
So you expect this will lead to extra resources and support for men studying medicine?
Are there similar parallels between things like race and cultural background? I'm asking out of ignorance and curiosity.
We think so, there's a bunch of studies showing black patients have better outcomes with black doctors.
Hard to pin down the why. There's subconscious systemic racism, particularly in medicine, and then there's the reaction to that. Do black people trust and obey black doctors more? Are non black doctors still believing myths about black tolerance to pain and other subconscious biases? Probably all of the above.
Studies like these are always hard to read. You can’t understand causal impact with getting a wide view of different studies, but there no world where any team of researchers with common sense will publish a study showing that male doctors are more effective. So you have publication bias. (I’ve published research, researchers are definitely sensitive to aesthetics).
Next you have the challenge of motivating that hospitals with more female doctors are not better for reasons correlated but unrelated to female doctors, like they have better work life balance which means doctors that are working under healthier conditions
To be clear, I’m not ruling out the possibility that a well designed study will show that female doctors are more effective. However I think the ethical use of the research is to understand what mindsets, habits, and competencies those doctors are applying that make them more effective. Then teach all doctors those. It doesn’t benefit society to highlight how “x” group of people, based on immutable or difficult to change traits , is superior to “y”. Ideas like that tend to evolve into unsettling behaviors and beliefs systems.
I can't imagine a study coming out saying "white doctors are more effective than black doctors". The idea would be immediately scrutinized and arguments about education, stress, and socioeconomic factors would immediately be brought up as the reasons why
There have been studies about black doctors being more effective at treating black patients than non-black doctors. But also race isn’t sex or gender, it’s apples and oranges. We don’t have different racial categories for sports because that would be ridiculous, there aren’t enough clear physical differences between “races” of people to make that necessary. But we split men and women’s sports because clearly there are some physiological differences between men and women - does any of that transfer over to medical care? I mean probably not, but I could see surgeons with smaller hands being more capable with some delicate surgical procedures.
Conversely, male sex is usually associated with risk taking behaviour. While I understand the authors grouping surgeries by speciality, I don't see why they wouldn't report the contribution of each individual procedure, as not all of them have similar risks.
It would be interesting to see if male risk taking behavior is similar across all IQ levels. I haven't read the paper, but it could be that males are more risky in their specialty, which causes the higher rates of mortality, vs males just being worse at any surgery
I was thinking about this as well, but in regards to someone claiming that male doctors were better. I couldn't care less if one group is better. I've had every kind of doctor, they all seem the same to me.
And you need to find a more productive way to spend your time than thinking about black people 24/7. I can tell you’re obsessed
What? I'm saying if we flipped the study, it would immediately be called out for what it is: discrimination on immutable characteristics that is the least impactful factor. But a factor that is most easily used by BOTH sides.
And how does this comment make me obsessed with black people? I'd rather think about the people who are ruining society than one group of people
Not to mention 3% is so insignificant it’s barely worth mentioning
3% odds... risk may be even less.
EDIT: One of the studies cited in the paper puts the adjusted difference in 30-day mortality risk at -0.43% (95% CI, -0.57%, -0.28%)
Does this account for which service the surgery falls under or types of surgeries? I work in surgery. There is significantly less female surgeons in brain, spine, vascular, and orthopedics. There are more women in general surgery, oncology, ob/gyn, urology, and plastics. This is all just anecdotal from the 2000+ surgeries I've scrubbed into but its something ive noticed.
They adjusted for the specialty. The "morbidity risk profiles" part seems to refer to dividing gastrointestinal surgery into high and low risk. They don't specify how much each procedure contributed to their specialty. I can't remember if they specified a reason for choosing which procedures to include, but only certain procedures were selected (e.g., for neurosurgery, only craniotomy is included).
EDIT: "Commonly performed procedures associated with higher morbidity risk were included given the higher potential for association with perioperative team diversity."
Ding ding ding. But it doesn't fit with their narrative, and not like laymans would understand this bias. But hey, at least we can wear the "I am a male doctor, so you are more likely to die" badge from now on.
They controlled for procedure type
Just curious if male doctors are more likely to perform surgeries that have higher risk.
That would be an interesting angle. We definitely compete in the stupid/risk department in a lot of areas.
3% sounds like a margin of error to me.
Wait to hear the adjusted risk difference for 30-day mortality reported by another study:
-0.43% (95% CI, -0.57%, -0.28%)
I love all the comments in these threads that always assume scientists don't account for various factors that some random person on reddit just KNOWS is the real reason for this result. Then when you read the study, of course they did account for those factors.
For example: "Operations were grouped by specialty and morbidity risk profile."
It is fair enough to call out both those who criticise and those who accept the study without reading it, but several comments here make very valid observations about the study. The authors made some choices not properly addressed in the paper, and commenters have brought that up.
Some of the authors' choices are a bit odd, like building a DAG but not bothering to include it in the main paper or the supplementary material, given that part of the purpose of a DAG is to show your assumptions to other readers. Others are more questionable; socioeconomic status seems to have been considered earlier on as a covariate, but it's not included in the final model. As far as I can see, no explanation is given for their absence from the model. This is weird since socioeconomic status is higher in high-diversity hospitals. Others have suggested the surgeon's age, another valid observation.
Grouping procedures by speciality is a common choice (well, the grouping part), although not optimal. Just as when you group continuous variables, you're losing information. While I can see why the authors would do that, it's worthwhile to point out that given their sample size, they could have well included each procedure as an individual covariate. Given that even procedures within the same speciality have different risk profiles, I would have at least expected a table summarising the number and proportions of each procedure stratified by exposure as a supplemental table.
EDIT: There's also the suggestion of p-hacking by the authors since they constructed part of their hypothesis (the definition of exposed vs non-exposed) from the same data they are analysing. As stated in the paper:
"Restricted cubic splines demonstrated an inflection point at 35% (Fig. S2). This was chosen as the cut-off point[...]"
I couldn't find any evidence (please correct me if I'm wrong) that the authors used a sub-sample of their data to determine the cut-off point and then tested the hypothesis in a different subset, which would have been the appropriate procedure. As it is, the authors found a pattern in the data and tested the data for that pattern.
It is also fun to read comment of people who think accounting for various factors works flawlessly.
I haven't seen a single person in this thread make that case. But by all means please quote and call them out.
Not sure why that's your response to me though unless you are reading things I didn't actually say because you're determined to be offended.
Yeah, if you, a non-professional, can think of a potential data issue within 5 seconds, I guarantee the people who published this research in a scientific journal have considered it
So they corrected for age?
Why don’t you read the article and find out?
It was a rhetorical question. They didn't. The model they end up with doesn't really pass the smell test. If we make surgical teams majority female, post-op morbities drop to zero.. Really?
A lot of people don’t actually read the papers posted before speculation. Sorry for assuming. They do control for patient age. And post-op morbidity doesn’t drop to 0, but it does reduce the odds (slightly). I don’t see why it’s not credible.
No it's fair. I didn't read the paper. Skimmed it when you prodded tho. Truth is that you can get a lot of info and useful questions before even reading the abstract. For example this is tagged as social science but purports to be about medicine (not a good sign already). The title makes a controversial with a suspiciously small effect.. All of which makes it not very credible.
Anyway, the specific problem here is that they appear (maybe it's in the supplemental methods, lemme know) not to have accounted for a confounding factor that us rubes on reddit noticed and the authors can't really plead ignorance of. They even cited a paper on the link between the age of docs and patient outcomes (the 40th ref), but they only seem to have controlled for patient age, not doctor age.
Point is that older teams of doctors will skew more male and younger teams will skew more female. Older teams are, it seems, more likely to have issues. Because they aren't correcting for the age of the docs, what the report as a gender difference is just the same age-related trend but filtered through the authors' confirmation bias.
Basically anytime you see a very tiny effect from a social science paper trying to make sweeping claims about culture war topics, you shouldn't take them to be very credible.
e:typo
You should never make a conclusion about a paper based on its abstract or without reading it. I don’t know why whoever posted this tagged it as social science, but that redditor’s choice has nothing to do with the study or researchers themselves. The redditor who posted it created the title of this thread, not the researchers.
There could be a legitimate reason why they didn’t control for age. Perhaps they had a small range. Perhaps, contrary to random redditor speculation, there is no known association between doctor age and patient comorbidity. If there was, they certainly thought of it. The absence of it doesn’t prove anything. It certainly can’t allow you to conclude the finding doesn’t hold.
The authors haven’t turned this into a culture war issue. The way it was posted is leading people to conclude it is, and people don’t like the conclusion so they pick it apart with a rudimentary understanding of the science.
There could be a legitimate reason why they didn’t control for age. [...] If there was, they certainly thought of it.
This is a naive statement. If there's a legitimate reason for the authors not including age in their model, it's their duty to report it in the paper. If they don't, readers can criticise this. Critical appraisal of scientific articles is the cornerstone of science.
The absence of it doesn’t prove anything. It certainly can’t allow you to conclude the finding doesn’t hold.
It also precludes you from concluding the finding holds. Unless you can address all sources of confounding, either statistically or theoretically, it is irresponsible to accept a result as true.
[...] and people don’t like the conclusion so they pick it apart with a rudimentary understanding of the science.
The same goes for people indulging in their confirmation bias. Perhaps even worse. People who read the title and go, "Aha! I knew it", and then start dismissing any criticism with ad hominem attacks and deflections tend to be particularly toxic and lower the quality of any debate.
But it may not be a major relationship known in the literature, hence why it’s not reported. If a major control is left out, that could be a problem, but I don’t know that dr age is nor do we know the range of the age of doctors. It is unlikely it would have made it past reviewers if they ignored an obvious control. And yes, it is imperative to criticize articles. But if you don’t have a degree or familiarity in the area of research, it’s highly unlikely you know the literature as well as the authors and reviewers. Rather than immediately accepting it as gospel, you take it as preliminary evidence until additional research is conducted. What you’re engaging in is also confirmation bias - immediately accusing the authors of p hacking with no evidence. You haven’t even constructed a p curve.
I love more that not a single study can ever be correct because “correlation doesn’t equal causation.”
I swear there could be a study that said “Putting a grenade in your mouth and pulling the pin will explode your head and kill you” and there would still be a “correlation doesn’t equal causation” comment in the thread.
I love more that not a single study can ever be correct because “correlation doesn’t equal causation.”
I love even more that no one here has commented anything along those lines (at least at the time I'm writing this comment)
I'd some one had a 3% difference in male grenades and female gredandes.. I'd be suspicious that it really is much much of a difference an it's more about some wierd grenade gender policy that the authors are interested in driving the work rather than the data.
Only a layman would trust scientific articles completely. Science is a rigorous process we need to be skeptical even after it have been published. And if a rogue article run loose we could have serious trouble correcting the consequence.
Remember the "vaccine cause autism" paper? Yeah, did the author account for the damage that it would have later caused?
And of course we as a human have bias and I have a big ass bias in this matter, I am a male doctor. So take my words also with a big grain of salt.
No one is talking about taking one scientific study as gospel. I don't need a lecture about something that didn't happen.
Every. Single. Time.
I replied this point to a comment. It is not my intention to spam, but I think it is relevant to the discussion, and I don't see it mentioned elsewhere.
The authors seemed to have used the SAME data they were analysing to construct their hypothesis. In particular, the authors used the data to define the exposed and unexposed groups. From the paper:
"Restricted cubic splines demonstrated an inflection point at 35% (Fig. S2). This was chosen as the cut-off point[...]"
The authors found a pattern in the data, tested the hypothesis that this pattern existed in the data, and presented this as the result of their PRIMARY ANALYSIS. I don't assume any malicious intent from the authors as this is something that unfortunately occurs often.
The correct approach would have been to select a cut-off point a priori based on previous evidence. However, I understand that the literature may still be in its infancy and that determining a "critical mass" is relevant to the question as per the paper's discussion. The correct approach, in this case, would have been extracting a sub-sample of the data to generate the cut-off points, discard this sub-sample and test their hypothesis using the remaining data set. I don't see any evidence of the authors doing this on the main paper or supplementary material.
As it is, we can't conclude whether the authors' main result is spurious.
EDIT: Edited for clarity (hopefully)
has there been any recent study published that says men perform better in literally anything
seems like every single gender study claims that women perform every task at a higher level than men
From NBC News Article: Having a female doctor could lower the risk of death or major health issues after surgery or hospitalization, studies have shown over and over.
The latest evidence, published Wednesday in the British Journal of Surgery, finds that surgical teams with more women see fewer health complications among patients than male-dominated teams.
The study compared hospitals in Canada where female surgeons and anesthesiologists made up more than 35% of the surgical teams to hospitals with a smaller share of female doctors. Higher levels of gender diversity were associated with a 3% lower chance of serious health complications for patients within three months of a major, non-emergency surgery.
The findings are based on an analysis of more than 700,000 procedures at 88 hospitals in Ontario between 2009 and 2019.
A handful of studies over the last decade have similarly shown that female doctors have better patient outcomes.
And no studies have show the opposite, right?
It would be interesting to perform a p-curve to get a sense of whether selective reporting or p-hacking can be ruled out.
EDIT: I can't get the hang of how markdown links work here =S
Better doctors take harder cases, harder cases have more complications, more complications mean more deaths.
Poor quality study. Why would a journal publish this? Male surgeons take on riskier cases that need to be done. Broad surgical disciplines were apparently taken into account but indivual types of procedures were not. Best to taken one type of procedure I.e. a Whipples and compare that.
Check out the controls they included
This is surgery teams, surgeons can pick if they work on a patient or not. If they feel the patient is a risk they can refuse to do the surgery so that if they died it wouldn't count against them. This could indicate that they are more selective and less likely to take risks, resulting in better survival rates but less lives changed if the surgery worked.
Which is why I find it odd they only give numbers by specialty, not by procedure.
Were the patients male or female?
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17d
Did you see the thread about hunting on this subreddit? Yes, ancient women would have been opportunistic hunters, just like ancient men wouldn't walk by a berry bush since it's a "job for women". No, a female hunter using a spear or ancient bow couldn't be as effective for big game hunting
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17d
its so funny how men like you throw hissy fits when reality doesnt fit your fantasy of persecution
Really can't wait for spacex to make Mars habitable for humans.
So many people butthurt by this and trying to justify men’s short comings.
If you want to do that. First take a look at your insecurity issues.
This is a great finding! Hopefully it can encourage more women to be in this field in the future
So many people falling for confirmation bias.
Instead of doing that. First take a critical look at the paper.
Hopefully, we can encourage more impartial research to improve the care of patients in the future.
I wonder if it is more related to female doctors, or is it that the hospitals who have more female doctors are generally better hospitals. As in a hospital in a more rough part of the world would have more male doctors and a hospital in more privileged part would have more female.
More diverse hospitals had a higher proportion of patients from better socioeconomic status, while less diverse hospitals had a higher proportion of patients from more deprived socioeconomic status. From the main paper and supplementary material, it would seem that socioeconomic status was initially supposed to be adjusted for, but the final results don't adjust for it. No explanation is given for this.
exactly, you can set up a hospital at a rich place with rich patients and 60% women and other is at middle of gang activity with 90% male and you can say patients with female care have better outcome
This paper is such rubbish, im off this sub
I have to say this here but with the ongoing feminization of medicine and academics in general you will most likely only have female doctors in the future and it feels like an article published to make the situation seem better than it is and where we’re heading to.
As more female doctors tend to work part time and cause staff shortages unfortunately.
I think universities will have to bite the bullet and start making efforts to make medicine appealing to the male demographic if you want to be able to get treatment in the future. Weather or not it’s the popular thing to do….well
And before some of you jump to conclusions: when the future and quality of healthcare systems around the world are in question it’s not a question of ideology and gender inequality rather hard, cold facts. Nobody should have to wait or not get the treatment needed and it could very well be you next affected.
I wish we could stop trying to please special interest groups and focus on research. Whatever the result is, as long as it is done ethically.
Only problem is you can skew the results in research and make claims like these for hospital board members to use as a marketing tool. If you comply by all ethical standards but still publish papers like these chances are some news outlet will write about it.
Most people can’t judge studies properly, just don’t forget that.
Were the women surgically removing an appendix while the males were removing brain tumors? You have to compare apples to apples.
They controlled for procedure
They controlled by specialty. They avoided talking about individual procedures.
Also, no brain tumour removal, only craniotomies.
You’re right, it’s more accurate to note directly from the article: “Operations were grouped by specialty and morbidity risk profile. Surgeon, anaesthetist, and hospital annual volume of procedures of interest, and hospital setting (academic versus community) were also captured”
Operations were grouped by speciality and morbidity risk profile
This sentence kinda irks me. As far as I can see, the morbidity risk profile thing only refers to GI surgery being split between high and low risk unless I'm missing something. Maybe it's just me, but that phrasing gives the impression that more information was entered into the model than what actually was.
My impression is it’s a common term used in their field, just jargon
great, they deserve more pay
That's a funny way to say "female surgeons have a lower rate of complications".
Now check what percentage of malpractised patients are women, and what the correlation is between being a woman, having an older male doctor, and getting seriously malpractised.
Speaking from my own experiences, male doctors are always such a coin flip. Female doctors tend more towards a higher degree of empathy and willingness to give me the time of day, that too many male doctors just lack.
By male doctors, you mean ego, and by female doctors, you mean super-ego. My last male doctor said about one of my complaints, "...well, you're getting older... " (I'm 35 now) as though his own personal sense of defeat was speaking to me. I was mortified because.... I don't want to hear a doctor "talking me into" accepting ANY declines in my functionality or performance. It's like he's preparing me for hospice brochures. I'M 35
Not scientific but this goes with my gut. When a person has to try harder to go as far as the average person (typically a white male) in their field, they are likely not to rest on their assumption of belonging. That's why if I board a flight with two women pilots I'm like -- we're in great hands. Again, not at all scientific and there are exceptions to the rules. But it's what my gut says.
For every 1,000,000 flights, 0.06 planes crash, and 90% of all aircraft pilots are white males according to the federal bureau of labor statistics. You chose a terrible example.
To the surprise of absolutely nobody with an ounce of common sense:
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