Adherence to Standards for Reporting Diagnostic Accuracy in Emergency Medicine Research
Bibliographic record
Abstract
BACKGROUND: Diagnostic tests are used frequently in the emergency department (ED) to guide clinical decision making and, hence, influence clinical outcomes. The Standards for Reporting of Diagnostic Accuracy (STARD) criteria were developed to ensure that diagnostic test studies are performed and reported to best inform clinical decision making in the ED. OBJECTIVE: The objective was to determine the extent to which diagnostic studies published in emergency medicine journals adhered to STARD 2003 criteria. METHODS: Diagnostic studies published in eight MEDLINE-listed, peer-reviewed, emergency medicine journals over a 5-year period were reviewed for compliance to STARD criteria. RESULTS: A total of 12,649 articles were screened and 114 studies were included in our study. Twenty percent of these were randomly selected for assessment using STARD 2003 criteria. Adherence to STARD 2003 reporting standards for each criteria ranged from 8.7% adherence (criteria-reporting adverse events from performing index test or reference standard) to 100% (multiple criteria). CONCLUSION: Just over half of STARD criteria are reported in more than 80% studies. As poorly reported studies may negatively impact their clinical usefulness, it is essential that studies of diagnostic test accuracy be performed and reported adequately. Future studies should assess whether studies have improved compliance with the STARD 2015 criteria amendment.
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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 | 0.689 | 0.822 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| 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; 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".