The Use of Reporting Guidelines in Rheumatology: A Cross-Sectional Study of Over 850 Manuscripts Published in 5 Major Rheumatology Journals
Bibliographic record
Abstract
OBJECTIVE: To assess whether 16 of the Enhancing the Quality and Transparency of Health Research (EQUATOR) Network-related reporting guidelines were used in rheumatology publications. METHODS: This was a cross-sectional study of research articles published in 5 high-performance rheumatology-focused journals in 2019. All articles were (1) manually reviewed to assess whether the use of a reporting guideline could be advisable, and (2) searched for the names and acronyms (eg, CONSORT [Consolidated Standards of Reporting Trials], STROBE [Strengthening the Reporting of Observational Studies in Epidemiology]) of 16 reporting guidelines. To calculate the "advisable use rate," the number of articles for which a guideline was used was divided by the number of articles for which the guideline was advised. Descriptive statistics were used. RESULTS: We reviewed 895 manuscripts across the 5 journals. The use of a guideline was deemed advisable for 693 (77%) articles. Reporting guidelines were used in 50 articles, representing 5.6% of total articles and 7.2% (95% CI 5-9) of articles for which guidelines were advised. The advisable use rate boundaries within which a guideline was applied by the journals were 0.03 to 0.10 for any guideline, 0 to 0.26 for CONSORT, 0.01 to 0.07 for STROBE, 0 to 0.8 for Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), and 0 to 0.14 for Animal Research: Reporting of In Vivo Experiments (ARRIVE). No identifiable trends in the variables studied were observed across the 5 journals. CONCLUSION: The limited use of reporting guidelines appears counterintuitive, considering that guidelines are promoted by journals and are intended to help authors report relevant information. Whether this finding is attributable to issues with the diffusion, awareness, acceptance, or perceived usefulness of the guidelines remains to be clarified.
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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 | 0.249 | 0.585 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.028 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| 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; 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".