A unified framework for bias assessment in clinical research
Bibliographic record
Abstract
Methodological flaws, limitations, and inadequate practices in research are well known and pose threats to the internal validity of any research study. However, there are ways of safeguarding research conduct to reduce the chance of research producing distorted results. Numerous tools now exist to assess the incorporation of such safeguards into primary research studies (also known as quality and/or risk-of-bias assessment). These tools typically include a variety of items that are then checked against those implemented in the study. Despite a lot of research in this area, no comprehensive generic classification of safeguards across study designs exist, although attempts have been made to clarify aspects of this. We review the developments in this area as well as use preliminary data from 100 methodological studies to illustrate our proposed approach. We conclude by proposing a new framework for identifying research studies at risk of being biased and the information in this article will promote a unification of the diverse approaches to facilitating bias assessment in clinical research.
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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 | 0.658 | 0.621 |
| Meta-epidemiology (narrow) | 0.009 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.021 |
| Bibliometrics | 0.047 | 0.023 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.028 | 0.018 |
| Open science | 0.017 | 0.022 |
| Research integrity | 0.021 | 0.025 |
| Insufficient payload (model declined to judge) | 0.008 | 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, 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".