Bibliographic record
Abstract
OBJECTIVE: The aim of this article is to offer recommendations and rationale for gender-specific antipsychotic treatment. METHOD: The author summarizes reviews of recent literature in psychiatric clinical trials, pharmacology, drug safety, toxicology, obstetrics and gynecology, and pediatrics. RESULTS: The pharmacokinetics and pharmacodynamics of antipsychotic drugs differ in women and men and are influenced by gender-specific factors such as body build, diet, smoking, concurrent medication, exercise, substance use, and hormonal transitions. In general, and for some drugs in particular, women require lower doses in order to stay well. Because preliminary drug testing is not done in pregnant women, the issue of effective dosing during pregnancy is unstudied, and safety for fetuses and nursing infants may not become evident until a drug is widely used. Specific adverse effects on issues crucial to women (e.g., parenting) have not been well studied, but some side effects, such as weight gain, passivity, hypotension, and hyperprolactinemia, are reported to be particularly problematic for women. Some serious side effects are more often seen among women than among men. CONCLUSIONS: Optimal maintenance regimens of antipsychotics for women and men are not the same.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".