PRISMA 2020 statement and guidelines for systematic review and meta-analysis articles, and their underlying mathematics: Life Cycle Committee Recommendations
Bibliographic record
Abstract
The paper discussed the overall aspects of systematic review and meta-analysis and explored the main mathematical context of meta-analysis. This included various methods of analyzing the effect size and obtaining the average effect size depending on the variable type. In doing so, methods of obtaining effect sizes in continuous outcome variables, categorical outcome variables, and correlation coefficients in studies were reviewed. In order to average the effect size, a fixed-effect model and a random effect model can be used. Finally, a forest plot can be drawn to visualize and interpret the results. Through this series of processes, researchers will be able to develop a better understanding of the systematic review and meta-analysis and perform a meta-analysis of their own.
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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 | 0.431 | 0.582 |
| Meta-epidemiology (narrow) | 0.005 | 0.010 |
| Meta-epidemiology (broad) | 0.009 | 0.034 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.015 | 0.010 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.037 | 0.033 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".