Assessing Risk of Bias and Confounding in Observational Studies of Interventions or Exposures: Further Development of the RTI Item Bank
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objectives To develop a framework for the assessment of the risk of bias and confounding against causality from a body of observational evidence, and to refine a tool to aid in identifying risk of bias, confounding, and precision in individual studies. Methods In conjunction with a Working Group, we sought to develop an overarching approach to assess the effect of confounding across the body of observational study evidence and within individual studies. We sought feedback from Working Group members on critical sources of bias most common to each observational study design type. We then refined and reduced the set of “core” questions that would most likely be necessary for evaluating risk of bias and confounding concerns for each design and refined the instructions provided to users to improve clarity and usefulness. Results We developed a framework that identifies additional steps necessary to evaluate the validity of causal claims in observational studies of benefits and harms from interventions. With the help of the Working Group, we narrowed the list of RTI Item Bank questions for evaluating risk of bias and precision from 29 to 16. Working Group members also provided their opinion of the most important questions for assessing risk of bias for four common observational study design types. Conclusions Attributing causality to interventions from such evidence requires prespecification of anticipated sources of confounding prior to the review, followed by appraisal of potential confounders at three levels: outcomes, studies, and the body of evidence. We propose a substantial expansion in the critical appraisal of confounding when systematic reviews include observational studies for evaluation of benefits or harms of interventions. Questions about burden, reliability, and validity remain to be answered. Consensus around specific items necessary for evaluating risk of bias for different types of observational study designs does not yet exist.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it