Evaluation of Imaging Research Adherence to the STARD 2015 Reporting Guideline: Update 9 Years After Implementation and Baseline Assessment
Bibliographic record
Abstract
Background: Adherence of diagnostic accuracy imaging research to the STARD 2015 reporting guideline was assessed at baseline in 2016; on average, only 55% of 30 items were reported. Several knowledge translation strategies have since been implemented by the STARD group. Purpose: The purpose of this study was to evaluate the adherence of diagnostic accuracy studies recently published in imaging journals to STARD 2015, to assess for changes in the level of adherence relative to the baseline study. Methods: We performed an electronic search on MEDLINE for diagnostic accuracy studies, published between May and June of 2024, from a select group of imaging journals. The timespan was modulated to achieve a sample size of 100 to 150 included studies. Overall and item-specific adherence to STARD 2015 was evaluated, in addition to associations with journal of publication, imaging modality, study design, country of corresponding author, imaging subspecialty area, journal impact factor, and journal STARD adoption. Statistical comparison to the baseline study from 2016 was also performed. Poisson Regression and two-tailed student’s tests were used to compare STARD adherence relative to variables included in subgroup analysis. Results: In the 126 included studies, average adherence to STARD 2015 was 61% (18.3/30 items; SD = 3.1), improved compared to the baseline study (55%; 16.6/30 items; SD = 2.2; P < .0001). Studies published in higher impact factor journals reported more items than those in lower impact factor journals (20.6 vs 18.4 items, P -value <.0001). There was no significant association between reporting completeness and journal of publication ( P = .7), imaging modality ( P = .21), country of corresponding author ( P = .46), imaging subspecialty ( P = .31), and journal STARD adoption status ( P = .55). Conclusion: Recently published diagnostic accuracy studies reported more STARD 2015 items than studies published in 2016, but completeness of reporting is still not optimal.
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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 | 0.175 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".