Common tool structures and approaches to risk of bias assessment: implications for systematic reviewers
Bibliographic record
Abstract
There are numerous tools available to assess the risk of bias in individual studies in a systematic review. These tools have different structures, including scales and checklists, which may or may not separate their items by domains. There are also various approaches and guides for the process, scoring, and interpretation of risk of bias assessments, such as value judgments, quality scores, and relative ranks. The objective of this commentary, which is part of the JBI Series on Risk of Bias, is to discuss some of the distinctions among different tool structures and approaches to risk of bias assessment and the implications of these approaches for systematic reviewers.
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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 | 0.841 | 0.956 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.017 | 0.018 |
| Bibliometrics | 0.063 | 0.071 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.040 | 0.032 |
| Open science | 0.017 | 0.021 |
| Research integrity | 0.023 | 0.031 |
| Insufficient payload (model declined to judge) | 0.007 | 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, 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".