Assessing the quality of studies in meta‐research: Review/guidelines on the most important quality assessment tools
Bibliographic record
Abstract
Systematic reviews and meta-analyses pool data from individual studies to generate a higher level of evidence to be evaluated by guidelines. These reviews ultimately guide clinicians and stakeholders in health-related decisions. However, the informativeness and quality of evidence synthesis inherently depend on the quality of what has been pooled into meta-research projects. Moreover, beyond the quality of included individual studies, only a methodologically correct process, in relation to systematic reviews and meta-analyses themselves, can produce a reliable and valid evidence synthesis. Hence, quality of meta-research projects also affects evidence synthesis reliability. In this overview, the authors provide a synthesis of advantages and disadvantages and main characteristics of some of the most frequently used tools to assess quality of individual studies, systematic reviews, and meta-analyses. Specifically, the tools considered in this work are the Newcastle-Ottawa scale (NOS) and the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) for observational studies, the Consolidated Standards of Reporting Trials (CONSORT), the Jadad scale, the Cochrane risk of bias tool 2 (RoB2) for randomized controlled trials, the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) and the Assessment of Multiple Systematic Reviews 2 (AMSTAR2), and AMSTAR-PLUS for meta-analyses. WHAT IS ALREADY KNOWN?: The informativeness and quality of evidence synthesis inherently depend on the quality of what has been pooled into meta-research projects. Beyond the quality of included individual studies, only a methodologically correct process, in relation to systematic reviews and meta-analyses themselves, can produce a reliable and valid evidence synthesis. WHAT IS NEW?: In this overview, the authors provide a synthesis of advantages and disadvantages and main characteristics of some of the most frequently used tools to assess quality of individual studies, systematic reviews, and meta-analyses. POTENTIAL IMPACT: This overview serves as a starting point and a brief guide to identify and understand the main and most frequently used tools for assessing the quality of studies included in meta-research. The authors here share their experience in publishing several meta-research-related articles covering different areas of medical sciences.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
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 | 0.385 | 0.611 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.032 | 0.027 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.012 | 0.010 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".