Sex Differences Between Female and Male Individuals in Antipsychotic Efficacy and Adverse Effects in the Treatment of Schizophrenia
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Antipsychotics are core treatments for people living with psychotic disorders. Understanding individualised factors that influence both efficacy and adverse responses will improve outcomes. The objective of this study was to examine sex differences in antipsychotic-related efficacy and tolerability. METHODS: This was a secondary analysis of data from phase 1 and 1a of Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE); participants with schizophrenia were randomly assigned to double-blinded treatment with oral olanzapine, quetiapine, risperidone, ziprasidone or perphenazine. Measures included Positive and Negative Syndrome Scale (PANSS), Clinical Global Impressions (CGI) scale and Calgary Depression Rating Scale, as well as self-reported side effects, medication compliance, dosage, weight measurements and various blood parameters. RESULTS: There were 1460 participants including 380 female and 1080 male individuals. Very few differences existed between male and female participants in response, adverse reactions, compliance or antipsychotic dosage. However, significantly more female participants than male participants reported constipation (28% vs 16%), dry mouth (50% vs 38%), gynecomastia/galactorrhea (11% vs 3%), incontinence/nocturia (16% vs 8%) and self reported weight gain (37% vs 24%) [all p < 0.001]. Within the risperidone treatment group, there was a significantly greater increase in prolactin levels (p < 0.001) among female participants (n = 61) than male participants (n = 159). No overall differences in clinician-rated measures, weight gain or other laboratory indicators were found. CONCLUSIONS: While overall sex differences were limited across efficacy and tolerability for antipsychotic treatment, there were some specific findings with risperidone. Further examination of sex differences within antipsychotic trials will be important to improve efficacy and reduce adverse responses across as well as individualising care for people with schizophrenia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.000 |
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".