Quality of Reporting of Diagnostic Accuracy Studies: No Change Since STARD Statement Publication—Before-and-after Study
Bibliographic record
Abstract
PURPOSE: To determine the quality of reporting of diagnostic accuracy studies before and after the Standards for Reporting of Diagnostic Accuracy (STARD) statement publication and to determine whether there is a difference in the quality of reporting by comparing STARD (endorsing) and non-STARD (nonendorsing) journals. MATERIALS AND METHODS: Diagnostic accuracy studies were identified by hand searching six STARD and six non-STARD journals for 2001, 2002, 2004, and 2005. Diagnostic accuracy studies (n = 240) were assessed by using a checklist of 13 of 25 STARD items. The change in the mean total score on the modified STARD checklist was evaluated with analysis of covariance. The change in proportion of times that each individual STARD item was reported before and after STARD statement publication was evaluated (chi(2) tests for linear trend). RESULTS: With mean total score as dependent factor, analysis of covariance showed that the interaction between the two independent factors (STARD or non-STARD journal and year of publication) was not significant (F = 0.664, df = 3, partial eta(2) = 0.009, P = .58). Additionally, the frequency with which individual items on the STARD checklist were reported before and after STARD statement publication has remained relatively constant, with little difference between STARD and non-STARD journals. CONCLUSION: After publication of the STARD statement in 2003, the quality of reporting of diagnostic accuracy studies remained similar to pre-STARD statement publication levels, and there was no meaningful difference (ie, one additional item on the checklist of 13 of 25 STARD items being reported) in the quality of reporting between those journals that published the STARD statement and those that did not.
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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 | 0.320 | 0.599 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".