MATES: A tool for appraising the completeness with which a meta-analysis has been reported
Bibliographic record
Abstract
Meta-analysis is commonly a core component of systematic reviews and has become an important method to reconcile conflicting findings, increase statistical power, and chart new research directions. However, poor reporting practices make it challenging to evaluate the validity of meta-analyses. Despite the existence of reporting checklists, a specifically designed tool has yet to be developed to appraise the completeness with which a meta-analysis has been reported. To bridge this gap, we introduce the Meta-analysis Appraisal Tool for Environmental Sciences (MATES). To develop MATES, we adapted a Delphi process involving experts in meta-analysis methodologies, researchers with experience in guideline/appraisal tool development, and editors of relevant journals. The Delphi process had five steps, including three workshops (11-16 participants), a survey (193 participants), and a validation task (30 participants). This iterative development process resulted in a 14-item appraisal tool that reflects the environmental science and research syntheses community's consensus on essential elements to appraise the completeness with which a meta-analysis has been reported. Validation across 50 meta-analyses demonstrated that the tool is repeatable (average agreement rate: 88.97 %) and time-efficient to implement (17.00 ± 12.23 min). We also outline guidance for interpreting MATES results, describe its potential applications, and reflect on the development process. The authors provide practical implementation guidance for each MATES item, illustrated with real examples in the supplementary material. We also report an extended development methodology to support reproducibility. Finally, we built created a ShinyApp that includes both a training module and an application tool to enhance the usability of MATES (https://kylemorrisonisshiny99.shinyapps.io/MATES_shiny/). Overall, MATES provides authors, readers, stakeholders, and editors with a reliable and accessible tool for appraising the completeness with which a meta-analysis in environmental sciences has been reported.
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How this classification was reachedexpand
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 | 0.033 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 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 teacher head, 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".