Bibliographic record
Abstract
Background: Animal and human experiments have confirmed sex differences in the expression of hepatic enzymes that metabolize antipsychotic drugs and that may, in this way, be partly responsible for the clinical sex/gender differences observed in the efficacy and tolerability of antipsychotic treatment. Aim: The aim of this mini review is to synthesize the literature on the pharmacodynamics of male/female differential response to antipsychotic drugs. Method: Relevant search terms were used to search for pre-clinical and human trials and analysis of antipsychotic differential drug response and occurrence/severity of adverse effects in women and men. Results: The search found that sex influences drug response via the amount of a given drug that enters the brain and the number of neurotransmitter receptors to which it can bind. Consequently, sex partly determines the efficacy of a specific drug and its liability to induce unwanted effects. There are other factors that can overshadow or enhance the dimorphic effect of sex, for instance, the host's age, hormonal status, diet and life style as well as the molecular structure of the drug and its dose, and the method of its administration. Most of all, the host's individual genetics affects each step of a drug's pharmacodynamics. Conclusion: On average, women's psychotic symptoms respond to antipsychotic drugs at doses lower than men's. This means that many women may be de facto overdosed and, thus, experience unnecessary adverse effects. That being said, factors such as genetics and age probably determine drug response and tolerability to a greater degree than do biological sex or gender social roles.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".