Evaluation of adherence to STARD for abstracts in a diverse sample of diagnostic accuracy abstracts published in 2012 and 2019 reveals suboptimal reporting practices
Bibliographic record
Abstract
OBJECTIVES: To evaluate the completeness of reporting in a sample of abstracts on diagnostic accuracy studies before and after the release of Standards for Reporting of Diagnostic Accuracy Studies (STARD) for abstracts in 2017. METHODS: We included 278 diagnostic accuracy abstracts published in 2012 (N = 138) and 2019 (N = 140) and indexed in EMBASE. We analyzed their adherence to 10 items of the 11-item STARD for abstracts checklist, and we explored variability in reporting across abstract characteristics using multivariable Poisson modeling. RESULTS: Most of the 278 abstracts (75%) were published in discipline-specific journals, with a median impact factor of 2.9 (IQR: 1.9-3.7). The majority (41%) of abstracts reported on imaging tests. Overall, a mean of 5.4/10 (SD: 1.4) STARD for abstracts items was reported (range: 1.2-9.7). Items reported in less than one-third of abstracts included 'eligible patient demographics' (24%), 'setting of recruitment' (30%), 'method of enrollment' (18%), 'estimates of precision for accuracy measures' (26%), and 'protocol registration details' (4%). We observed substantial variability in reporting across several abstract characteristics, with higher adherence associated with the use of a structured abstract, no journal limit for abstract word count, abstract word count above the median, one-gate enrollment design, and prospective data collection. There was no evidence of increase in the number of reported items between 2012 and 2019 (5.2 vs 5.5 items; adjusted reporting ratio: 1.04 [95% CI: 0.98-1.10]). CONCLUSION: This sample of diagnostic accuracy abstracts revealed suboptimal reporting practices without improvement between 2012 and 2019. The test evaluation field could benefit from targeted knowledge translation strategies to improve completeness of reporting in abstracts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.305 | 0.742 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.007 |
| 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; 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".