Stratification by Sex and Hormone Level When Contrasting Men and Women in Schizophrenia Trials Will Improve Personalized Treatment
Bibliographic record
Abstract
BACKGROUND: Sex and gender differences have been reported in the prevalence, expression, treatment response, and outcome of schizophrenia, but most reports are based on relatively small samples that have not been stratified for the impact of sex hormone levels. This literature review aims to show how women's hormone levels can impact the results of male/female comparisons. METHODS: This is a narrative review of data from publications of the last decade. RESULTS: Epidemiologic evidence, reports of the impact of hormones on cognition, results of sexually dimorphic responses to treatment, and male/female trajectories of illness over time all suggest that female hormone fluctuations exert major effects on male/female differences in schizophrenia. CONCLUSIONS: Information on hormonal status in women participants is rarely available in clinical studies in schizophrenia, which makes male/female comparisons largely uninterpretable. These are the current challenges. Opportunities for individualized treatment are growing, however, and will undoubtedly result in improved outcomes for both women and men in the future.
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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".