Considerations from the risk of bias perspective for updating Cochrane reviews
Bibliographic record
Abstract
Authors of Cochrane reviews are expected to update their reviews every 2 years. The updating process helps to ensure that reviews are current and include recent evidence. However, the updating process is time-consuming for authors, particularly when Cochrane methods evolve and authors are required to revisit some of the originally included studies.The Cochrane Collaboration's 'Risk of bias' tool is a mandatory component of Cochrane reviews, providing an assessment of the potential biases of included studies. The tool has been modified most recently in 2011, and the expectation is that new versions will continue to be produced and utilised in all Cochrane reviews. In this commentary we discuss, in the context of updating scenarios that are likely to be encountered, the potential options systematic review authors may have recourse to when the Cochrane Collaboration's 'Risk of bias' tool has been modified between the original review and its update. We recommend that authors who are updating reviews should revise their original assessments of included studies using the most recent version of the risk of bias tool. Despite the increased workload, use of the most recent version of the tool facilitates consistency of methods and reporting both across and within reviews, and ensures currency to the methodological rigour.
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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 | 0.749 | 0.929 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.018 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.007 | 0.047 |
| Scholarly communication | 0.039 | 0.058 |
| Open science | 0.025 | 0.018 |
| Research integrity | 0.075 | 0.063 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".