Risk factors for the late‐onset psychoses: a systematic review of cohort studies
Bibliographic record
Abstract
BACKGROUND: Psychoses with onset in late adulthood are challenging. Identifying those older patients at risk would be clinically important and would have research implications. METHODS: A computer search was performed to identify all cohort studies of risk factor(s) for psychotic symptoms or disorders with onset at 40 years or older. Experts were contacted and bibliographies were screened for additional references. Validity of located studies was assessed according to evidence-based medicine criteria for risk factors studies. Data were extracted and tabulated for qualitative and quantitative analyses. RESULTS: Twelve articles were retrieved, corresponding to 11 studies of 32 potential risk factors. In the qualitative analysis, only the history of psychotic symptoms, cognitive problems, poor health status, visual impairment, and negative life events appeared to be significant risk factors of late-onset psychosis. Older age, female gender, and hearing impairment were not associated with psychosis in older patients. Quantitative analysis was feasible with only one item, female gender, and confirmed the lack of associated risk with late-onset psychosis. CONCLUSIONS: Despite the methodological limitations of the studies included in this review, there is some evidence from cohort studies that history of psychotic symptoms, cognitive problems, poor physical health, visual impairment, and negative life events are risk factors for late-onset psychosis. More long-term follow-up studies are needed to confirm these findings.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".