How Age and Gender Predict Illness Course in a First-Episode Nonaffective Psychosis Cohort
Bibliographic record
Abstract
OBJECTIVE: Male gender and young age at onset of schizophrenia are traditionally associated with poor treatment outcome and often used to determine prognosis. However, many studies use nonincident samples and fail to adjust for symptom severity at onset. We hypothesized that age and gender would influence severity of presentation but would not predict outcome after adjustment for symptoms at presentation. METHOD: 628 people with first-episode ICD-9 and DSM-IV nonaffective psychosis from 2 historical cohorts recruited from sequential presentations in Canada and the United Kingdom (1996-1998) were assessed prospectively at presentation and over 12-18 months using the Positive and Negative Syndrome Scale (PANSS). RESULTS: Models of the age-at-onset distributions with 2 underlying modes at similar ages in women (ages 23 years and 47 years) and men (ages 22 years and 46 years) had relatively good fits compared to single-mode models (χ(2)1 better by 9.2 for females, 8.0 for males, both P < .05). At presentation, scores for negative symptoms were 1.84 points worse for males (95% CI, 1.05 to 2.58; P < .001) in a mixed effects model. Younger age also predicted higher negative scores at presentation (partial correlation r = -0.18, P < .01; P < .001 in the mixed effects model). Findings were similar for cognitive-disorganized symptoms. However, after controlling for baseline symptoms, age at onset and gender did not significantly predict subsequent symptom course in the mixed effects models. CONCLUSIONS: Gender and age at onset are independently associated with symptoms at presentation but not with medium-term course of schizophrenia. This finding reinforces the importance of early identification and prevention of severe negative symptoms at first episode, whatever an individual's age and gender.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".