Female psychopharmacology matters! Towards a sex-specific psychopharmacology
Bibliographic record
Abstract
There is increasing recognition that women have a higher prevalence of certain psychiatric illnesses, and a differential treatment response and course of illness compared to men. Additionally, clinicians deal with a number of disorders like premenstrual syndrome, premenstrual dysphoric disorder, and postpartum depression, which affect women specifically and for which treatment and biological pathways are still unclear. In this article we highlight recent research which suggests that different biological mechanisms may underlie sex differences in responsiveness to stress. Sex differences are evident at the receptor level; where the corticotropin-releasing factor receptor shows differential coupling to adaptor proteins in males and females. The neuropeptide oxytocin also shows sex-specific effects in a range of social behaviors. It may act as a biomarker in post-traumatic stress disorder where sex differences are evident. Studies in women using hormonal contraception show that some of these oxytocin-mediated effects are likely influenced by sex hormones. In female rats rapid changes in circulating progesterone levels are associated with exaggerated behavioral responses to mild stress and blunted responses to benzodiazepines that could be prevented by acute treatment with low-dose fluoxetine. Perceived barriers in research on women have hindered progress. The development of a sex-specific psychopharmacology as a basis for translating this type of research into clinical practice is vital to improve treatment outcomes for women.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".