Origin of Sex-Biased Mental Disorders: Do Males and Females Experience Different Selective Regimes?
Bibliographic record
Abstract
The origins of sex-biased differences in disease and health are of growing interest to both medical researchers and health professionals. Several major factors have been identified that affect sex differences in incidence of diseases and mental disorders. These are: sex chromosomes, sex hormones and female immunity, sexual selection and antagonistic evolution, and differential susceptibility of sexes to environmental factors. These factors work on different time scales and are not exclusive of each other. Recently, a combined Sexual Selection-Sex Hormones (SS-SH) Theory was presented as an evolutionary mechanism to explain sex-biased differences in diseases and mental disorders (Singh in J Mol Evol 89:195-213, 2021). In that paper disease prevalence trends were investigated, and non-sex-specific diseases were hypothesized to be more common in males than in females in general. They showed signs of exceptions to this trend with inflammatory diseases and stress-related mental disorders that were more common in females. We believe that the SS-SH theory requires the consideration of psycho-social stress (PSS) to explain the predominance of female-biased mental disorders and some other exceptions in their findings. Here we present a theory of sex-differential experience of PSS and provide quantitative support for the combined SS-SH-PSS Theory using age-standardized incidence rates (ASIRs) recording the levels of male- and female-bias in data obtained from different countries. The grand theory provides an evolutionary framework for explaining patterns of sex-biased trends in the prevalence of disease and health. Further exploration of women's vulnerability to social factors may help to facilitate new treatments for female-biased diseases.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".