Risk factors and peripheral biomarkers for schizophrenia spectrum disorders: an umbrella review of meta‐analyses
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 I2 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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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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.026 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".