Reporting of imaging diagnostic accuracy studies with focus on MRI subgroup: Adherence to STARD 2015
Bibliographic record
Abstract
PURPOSE: To evaluate adherence of diagnostic accuracy studies in imaging journals to the STAndards for Reporting of Diagnostic accuracy studies (STARD) 2015. The secondary objective was to identify differences in reporting for magnetic resonance imaging (MRI) studies. MATERIALS AND METHODS: MEDLINE was searched for diagnostic accuracy studies published in imaging journals in 2016. Studies were evaluated for adherence to STARD 2015 (30 items, including expanded imaging specific subitems). Evaluation for differences in STARD adherence based on modality, impact factor, journal STARD adoption, country, subspecialty area, study design, and journal was performed. RESULTS: Adherence (n = 142 studies) was 55% (16.6/30 items, SD = 2.2). Index test description (including imaging-specific subitems) and interpretation were frequently reported (>66% of studies); no important differences in reporting of individual items were identified for studies on MRI. Infrequently reported items (<33% of studies) included some critical to generalizability (study setting and location) and assessment of bias (blinding of assessor of reference standard). New STARD 2015 items: sample size calculation, protocol reporting, and registration were infrequently reported. Higher impact factor (IF) journals reported more items than lower IF journals (17.2 vs. 16 items; P = 0.001). STARD adopter journals reported more items than nonadopters (17.5 vs. 16.4 items; P = 0.01). Adherence varied between journals (P = 0.003). No variability for study design (P = 0.32), subspecialty area (P = 0.75), country (P = 0.28), or imaging modality (P = 0.80) was identified. CONCLUSION: Imaging accuracy studies show moderate adherence to STARD 2015, with only minor differences for studies evaluating MRI. This baseline evaluation will guide targeted interventions towards identified deficiencies and help track progress in reporting. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:523-544.
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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 | 0.517 | 0.759 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".