Assessing the Usability of the Risk Of Bias in Non-randomized Studies – of Interventions (ROBINS-I) Tool for Studies of Exposure and Intervention in Environmental Health Research
Bibliographic record
Abstract
Introduction: The Risk Of Bias in Non-randomized Studies – of Interventions (ROBINS-I) tool evaluates internal validity (risk of bias) in non-randomized studies of interventions in comparison to an ideal (hypothetical) randomized trial. The use of ROBINS-I in studies dealing with exposures or interventions in environmental health has not yet been explored. This study evaluated the usability and applicability of ROBINS-I in studies of environmental health (EH) exposure. Methods: Three researchers in sequential rounds applied ROBINS-I to three systematic reviews of EH exposures: bisphenol-A and obesity; perfluorooctanoic acid and birth weight; and polybrominated diphenyl ethers and thyroid function. We began by providing instructions for application of ROBINS-I to EH studies, including possible confounders and co-exposures specific to the exposures considered in the three reviews. For the first two rounds of testing, two reviewers independently applied ROBINS-I and provided feedback on usability of the tool. Barriers and facilitators to the appropriateness of ROBINS-I for environmental health were identified and modifications made to the tool, as necessary. For the third round of testing, three reviewers independently applied the tool and came to consensus on item-level and overall study risk of bias. Results: Suggested modifications ranged from syntax and wording to conceptual changes to the tool. The term “intervention” was replaced with “exposure” throughout the document. Additional instructions were provided to address assessment of cross-sectional studies. Fields to collect information on measurement of exposures and outcomes of interest was added to the project protocol. Additional granularity was added to the measurement of interventions/exposure domain. Conclusion: Modifications made to the risk of bias tool to tailor it to studies of EH exposure increased understanding and application of the tool, as well as consistency in responses.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.719 | 0.866 |
| Meta-epidemiology (narrow) | 0.008 | 0.006 |
| Meta-epidemiology (broad) | 0.017 | 0.044 |
| Bibliometrics | 0.035 | 0.025 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".