From critical appraisal to risk of bias assessment: clarifying the terminology for study evaluation in JBI systematic reviews
Bibliographic record
Abstract
The foundations for critical appraisal of literature have largely progressed through the development of epidemiologic research methods and the use of research to inform medical teaching and practice. This practical application of research is referred to as evidence-based medicine and has delivered a standard for the health care profession where clinicians are equally as engaged in conducting scientific research as they are in the practice of delivering treatments. Evidence-based medicine, now referred to as evidence-based health care, has generally been operationalized through empirically supported treatments, whereby the choice of treatments is substantiated by scientific support, usually by means of an evidence synthesis. As evidence synthesis methodology has advanced, guidance for the critical appraisal of primary research has emphasized a distinction from the assessment of internal validity required for synthesized research. This assessment is conceptualized and branded in various ways in the literature, such as risk of bias, critical appraisal, study validity, methodological quality, and methodological limitations. This paper provides a discussion of the definitions and characteristics of these terms, concluding with a recommendation for JBI to adopt the term "risk of bias" assessment.
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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 | 0.735 | 0.834 |
| Meta-epidemiology (narrow) | 0.008 | 0.006 |
| Meta-epidemiology (broad) | 0.024 | 0.016 |
| Bibliometrics | 0.043 | 0.046 |
| Science and technology studies | 0.008 | 0.073 |
| Scholarly communication | 0.043 | 0.034 |
| Open science | 0.019 | 0.030 |
| Research integrity | 0.031 | 0.045 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".