Letter: Robins-E risk of bias tool
Bibliographic record
Abstract
Risk-of-bias tools are increasingly used as part of systematic reviews, to help make a uniform evaluation of study quality across a variety of studies (NASEM 2021). Several well-known tools, including OHAT and the Navigation Guide, are used to evaluate observational epidemiologic studies (OHAT 2019, Woodruff and Sutton, 2014). A recently published tool called ROBINS-E (Higgins et al. 2024) was developed to evaluate observational studies on environmental and occupational exposures. We have concerns regarding how this tool can be appropriately used and how it relates to other approaches to evidence synthesis. We are a group of environmental and occupational epidemiologists/exposure experts, nearly all of whom took part in the early discussion and piloting of this new risk-of-bias tool and previously published our general views on risk-of-bias tools (Steenland et al. 2020).
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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 | 0.029 | 0.310 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.053 | 0.038 |
| Insufficient payload (model declined to judge) | 0.014 | 0.016 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".