Is It Important to Consider Sex and Gender in Neurocognitive Studies?
Bibliographic record
Abstract
Is it important to consider sex and gender in neurocognitive, neurobiological, and clinical studies?I refer here to "sex" as a biological variable related mainly to sex chromosomes and sex steroid hormones, while "gender" stands for a psychosociocultural construct related to gender role socialization, levels of masculinity and femininity, and stress related to adhesion to gender stereotypes.It is a rhetorical question and I hope that an overwhelming majority of readers would answer: "yes, of course!"However, despite the fact that more attention has been devoted over the past decade to delineating differences between men and women in their neuroanatomy, neurophysiology, and cognitive and emotional processing (1, 2), a substantial number of researchers (basic and clinical) continue ignoring "the second sex" and study exclusively males.The reasons are multiple and range from an unawareness, through a lack of sufficient funding to examine both sexes, to the argument that the studies, to date, have not found any significant sex differences in a given paradigm or disorder and therefore there is no need to include both males and females in a study.Finally, there is a group of theorists who sees this line of inquiry (i.e., investigating sex differences in the brain and cognition) as inherently biased, thus contributing to some harmful stereotypes that may lead to increasing gender inequalities [e.g., Ref. (3,4)].It is hard to argue with the lack of funds and indeed there are cognitive and clinical domains where we have not seen any indication of potential sex or gender differences.Nevertheless, our technology has improved and we possess more sensitive instrumentation, which may detect subtle differences that were not previously apparent.Moreover, we must remember that men and women of today are different from men and women of two or three decades ago.We are not only socialized differently, due to changed family values and education, more competitive job market and prominence of social media, but also exposed to more environmental toxins, including endocrine disruptors, which may affect our physical and mental health (5,6).As to the harmful effects of some studies of the neurobiological and cognitive sex differences, I agree that data are sometimes interpreted in a biased manner and may contribute to propagation of damaging gender stereotypes.However, I also believe that excluding women is much more dangerous.We have seen the harm done with several drugs released for treatment of various disorders without proper testing or consideration of women's physiology (e.g., Posicor, approved for the treatment of hypertension and angina, slowed or stopped the heart rate especially in elderly women; antihistamines such as Seldane and Hismanal induced cardiac arrhythmias disproportionally more frequently in women), but thankfully the situation is changing (7,8).While it is true that men and women are much more alike than different and it actually makes more sense to talk about sex and gender similarities (be it in brain structure or cognitive function), there are those subtle differences that may provide clues to disentangling etiology of some neurological and neuropsychiatric disorders, such as multiple sclerosis, autism, mood, and anxiety disorders, or shed a new light on treatment of these conditions.In short, "vive la similarité, but let's explore differences!"I would like to use two clinical examples that I am most familiar with, to illustrate my point -schizophrenia and drug addiction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.189 | 0.255 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".