Evaluating the Impact of Peer Review on the Completeness of Reporting in Imaging Diagnostic Test Accuracy Research
Bibliographic record
Abstract
BACKGROUND: Despite the nearly ubiquitous reported use of peer review among reputable medical journals, there is limited evidence to support the use of peer review to improve the quality of biomedical research and in particular, imaging diagnostic test accuracy (DTA) research. PURPOSE: To evaluate whether peer review of DTA studies published by imaging journals is associated with changes in completeness of reporting, transparency for risk of bias assessment, and spin. STUDY TYPE: Retrospective cross-sectional study. STUDY SAMPLE: Cross-sectional study of articles published in Journal of Magnetic Resonance Imaging (JMRI), Canadian Association of Radiologists Journal (CARJ), and European Radiology (EuRad) before March 31, 2020. ASSESSMENT: Initial submitted and final versions of manuscripts were evaluated for completeness of reporting using the Standards for Reporting Diagnostic Accuracy Studies (STARD) 2015 and STARD for Abstracts guidelines, transparency of reporting for risk of bias assessment based on Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2), and actual and potential spin using modified published criteria. STATISTICAL TESTS: Two-tailed paired t-tests and paired Wilcoxon signed-rank tests were used for comparisons. A P value <0.05 was considered to be statistically significant. RESULTS: We included 84 diagnostic accuracy studies accepted by three journals between 2014 and 2020 (JMRI = 30, CARJ = 23, and EuRad = 31) of the 692 which were screened. Completeness of reporting according to STARD 2015 increased significantly between initial submissions and final accepted versions (average reported items: 16.67 vs. 17.47, change of 0.80 [95% confidence interval 0.25-1.17]). No significant difference was found for the reporting of STARD for Abstracts (5.28 vs. 5.25, change of -0.03 [-0.15 to 0.11], P = 0.74), QUADAS-2 (6.08 vs. 6.11, change of 0.03 [-1.00 to 0.50], P = 0.92), actual "spin" (2.36 vs. 2.40, change of 0.04 [0.00 to 1.00], P = 0.39) or potential "spin" (2.93 vs. 2.81, change of -0.12 [-1.00 to 0.00], P = 0.23) practices. CONCLUSION: Peer review is associated with a marginal improvement in completeness of reporting in published imaging DTA studies, but not with improvement in transparency for risk of bias assessment or reduction in spin. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 1.
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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 | 0.839 | 0.954 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".