Global environmental risk factors of schizophrenia: a study protocol for systematic review and meta-analysis of cohort studies
Bibliographic record
Abstract
INTRODUCTION: Schizophrenia is a chronic, complex and severe psychiatric disorder affecting millions of people every year and inflicting different costs to the individual, family and community. A growing body of evidence has introduced several genetic and environmental factors and their interactions as aetiological factors of schizophrenia. The goal of this systematic review and meta-analysis is to present an updated representation of the global environmental risk factors of schizophrenia. METHOD AND ANALYSIS: This protocol is developed and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols guideline. We will systematically search the databases such as PubMed, Scopus, Web of Science, PsycINFO and Embase until 30 September 2022. We include Cohort studies that have reported one or more risk factors of schizophrenia. We will also search Google Scholar search engine and references lists of included articles. Extracting the relevant data and assessing the quality of the included studies will be independently performed by different authors of our team. The risk of bias for the included studies will be evaluated using Newcastle-Ottawa Scale. Subgroup analysis, meta-regression or sensitivity analysis will be our solution to deal with heterogeneity between studies. We will use a funnel diagram as well as Begg and Egger tests to check for possible publication bias. ETHICS AND DISSEMINATION: Ethical approval is not required because there will be no primary data collection or human involvement. The results of this study will be published in an international peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42022359327.
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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 | 0.094 | 0.137 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.022 | 0.028 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.055 | 0.006 |
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".