M29. Methodological Quality of Meta-Analyses on Cognition in Schizophrenia: A Systematic Review
Bibliographic record
Abstract
Background: Several meta-analyses have been conducted to determine the magnitude of cognitive deficits in patients with schizophrenia. The effect of several key variables—such as age, gender, education, duration of illness, symptomatology, patient status, psychiatric comorbidity, publication year and sample size—has also been evaluated with moderator analysis to understand the nature of the deficit. One overlooked methodological aspect to consider is the potential influence of methodological quality of the meta-analysis in order to draw valid inferences. The main objective of this systematic review of meta-analysis was to evaluate the methodological quality of the meta-analyses evaluating cognition in schizophrenia. Methods: Meta-analysis published between 1970 and October 2015 were identified according to the following criteria: (1) Group of patients with a diagnosis of schizophrenia and schizophrenia spectrum; (2) Comparative group (patients with schizophrenia or healthy control group); (3) Cognitive outcomes derived from neuropsychological tests; (4) Moderators analysis computed on cognition. Methodological quality assessment of included meta-analyses was carried out using R-AMSTAR (Revised Assessment of Multiple Systematic Reviews), which provides a rating ranging from 11 to 44 points. Results: A total of 34 meta-analyses were included in this systematic review: 16 meta-analyses comprised a healthy control group and 18 meta-analyses comprised a group of patients with schizophrenia. R-AMSTAR mean scores were 23.31 points (SD = 2.85; range = 18–28) and 25.44 points (SD = 4.69; range = 19–38) respectively, which indicates moderate quality. Conclusion: Few meta-analyses used guidelines such as PRISMA and MOOSE. A methodological guideline pointing out variables that could influence cognition in schizophrenia is highlighted while considering methodological quality of meta-analysis. Limits and recommendations on the methodological quality assessment of meta-analysis are also discussed.
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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 | 0.109 | 0.290 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.046 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".