Risk of bias assessment of test comparisons was uncommon in comparative accuracy systematic reviews: an overview of reviews
Bibliographic record
Abstract
OBJECTIVES: Comparative diagnostic test accuracy systematic reviews (DTA reviews) assess the accuracy of two or more tests and compare their diagnostic performance. We investigated how comparative DTA reviews assessed the risk of bias (RoB) in primary studies that compared multiple index tests. STUDY DESIGN AND SETTING: This is an overview of comparative DTA reviews indexed in MEDLINE from January 1st to December 31st, 2017. Two assessors independently identified DTA reviews including at least two index tests and containing at least one statement in which the accuracy of the index tests was compared. Two assessors independently extracted data on the methods used to assess RoB in studies that directly compared the accuracy of multiple index tests. RESULTS: We included 238 comparative DTA reviews. Only two reviews (0.8%, 95% confidence interval 0.1 to 3.0%) conducted RoB assessment of test comparisons undertaken in primary studies; neither used an RoB tool specifically designed to assess bias in test comparisons. CONCLUSION: Assessment of RoB in test comparisons undertaken in primary studies was uncommon in comparative DTA reviews, possibly due to lack of existing guidance on and awareness of potential sources of bias. Based on our findings, guidance on how to assess and incorporate RoB in comparative DTA reviews is needed.
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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 | 0.455 | 0.781 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.022 |
| Bibliometrics | 0.024 | 0.028 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".