Risk factors and peripheral biomarkers for schizophrenia spectrum disorders: an umbrella review of meta‐analyses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective This study aimed to systematically appraise the meta‐analyses of observational studies on risk factors and peripheral biomarkers for schizophrenia spectrum disorders. Methods We conducted an umbrella review to capture all meta‐analyses and Mendelian randomization studies that examined associations between non‐genetic risk factors and schizophrenia spectrum disorders. For each eligible meta‐analysis, we estimated the summary effect size estimate, its 95% confidence and prediction intervals and the I 2 metric. Additionally, evidence for small‐study effects and excess significance bias was assessed. Results Overall, we found 41 eligible papers including 98 associations. Sixty‐two associations had a nominally significant ( P ‐value <0.05) effect. Seventy‐two of the associations exhibited large or very large between‐study heterogeneity, while 13 associations had evidence for small‐study effects. Excess significance bias was found in 18 associations. Only five factors (childhood adversities, cannabis use, history of obstetric complications, stressful events during adulthood, and serum folate level) showed robust evidence. Conclusion Despite identifying 98 associations, there is only robust evidence to suggest that cannabis use, exposure to stressful events during childhood and adulthood, history of obstetric complications, and low serum folate level confer a higher risk for developing schizophrenia spectrum disorders. The evidence on peripheral biomarkers for schizophrenia spectrum disorders remains limited.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it