Sex differences in schizophrenia-spectrum diagnoses: results from a 30-year health record registry
Bibliographic record
Abstract
This study investigated sociodemographic and clinical differences between the sexes in individuals affected by schizophrenia-spectrum disorders (SSD) who accessed outpatient mental health services. Within a retrospective cohort of 45,361 outpatients receiving care in Ferrara (Italy) from 1991 to 2021, those with a SSD diagnosis were compared between the sexes for sociodemographic and clinical characteristics before and after the index date (when the ICD-9: 295.*diagnosis was first recorded) to assess early trajectory, age and type of diagnosis, and severity of illness indicated by medication use, hospitalization, and duration of psychiatric care. Predictors of discharge were also investigated. Among 2439 patients, 1191 were women (48.8%). Compared to men, women were significantly older at first visit (43.7 vs. 36.8 years) and at index date (47.8 vs. 40.6) with peak frequency at age 48 (vs. 30). The most frequent last diagnosis recorded before the index date was delusional disorder (27.7%) or personality disorder (24.3%) in men and depression (24%) and delusional disorder (30.1%) in women. After the index date, long-acting antipsychotics and clozapine were more frequently prescribed to men (46.5% vs. 36.3%; 13.2% vs. 9.4%, p < 0.05) and mood stabilizers and antidepressants to women (24.3% vs. 21.1%; 50.1% vs. 35.5%; p < 0.05). Women had fewer involuntary admissions (10.1% vs. 13.6%) and were more likely to be discharged as the time under care increased (p = 0.009). After adjusting for covariates, sex was not a significant predictor of discharge. Our study confirmed that sex differences exist in clinical and sociodemographic characteristics of outpatients with SSD and that gender considerations might influence the rapidity of diagnosis and medications prescribed. These findings highlight the need to implement a women-tailored approach in specialist care programs for psychoses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".