A risk of bias instrument for non-randomized studies of exposures: A users' guide to its application in the context of GRADE
Bibliographic record
Abstract
The objective of this paper is to explain how to apply, interpret, and present the results of a new instrument to assess the risk of bias (RoB) in non-randomized studies (NRS) dealing with effects of environmental exposures on health outcomes. This instrument is modeled on the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) instrument. The RoB instrument for NRS of exposures assesses RoB along a standardized comparison to a randomized target experiment, instead of the study-design directed RoB approach. We provide specific guidance for the integral steps of developing a research question and target experiment, distinguishing issues of indirectness from RoB, making individual-study judgments, and performing and interpreting sensitivity analyses for RoB judgments across a body of evidence. Also, we present an approach for integrating the RoB assessments within the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework to assess the certainty of the evidence in the systematic review. Finally, we guide the reader through an overall assessment to support the rating of all domains that determine the certainty of a body of evidence using the GRADE approach.
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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 | 0.333 | 0.666 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.035 | 0.013 |
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".