Endorsement of reporting guidelines and study registration by endocrine and internal medicine journals: meta-epidemiological study
Bibliographic record
Abstract
OBJECTIVES: To improve the trustworthiness of evidence, studies should be prospectively registered and research reports should adhere to existing standards. We aimed to systematically assess the degree to which endocrinology and internal medicine journals endorse study registration and reporting standards for randomised controlled trials (RCTs), systematic reviews (SRs) and observational studies (ObS). Additionally, we evaluated characteristics that predict endorsement of reporting or registration mechanism by these journals. DESIGN: Meta-epidemiological study. SETTING: Journals included in the 'Endocrinology and Metabolism' and 'General and Internal Medicine' 2017 Journal Citation Reports. PARTICIPANTS: Journals with an impact factor of ≥1.0, focused on clinical medicine, and those who publish RCTs, SRs and ObS were included. PRIMARY OUTCOMES: Requirement of adherence to reporting guideline and study registration as determined from the journals' author instructions. RESULTS: Of the 170 (82 endocrinology and 88 internal medicine) eligible journals, endorsing of reporting standards was the highest for RCTs, with 35 (43%) of endocrine journals and 55 (63%) of internal medicine journals followed by SRs, with 21 (26%) and 48 (55%), respectively, and lastly, by ObS with 41 (50%) of endocrine journals and 21 (24%) of internal medicine journals. In 78 (46%) journals RCTs were required to be registered and published in adherence to the Consolidated Standards of Reporting Trials statement. Only 11 (6%) journals required registration of SRs. Internal medicine journals were more likely to endorse reporting guidelines than endocrine journals except for Strengthening the Reporting of Observational Studies in Epidemiology. No other journal characteristic proved to be an independent predictor of reporting standard endorsement for RCTs besides trial registration. CONCLUSION: Our results highlight that study registration requirement and reporting guideline endorsement are suboptimal in internal medicine and endocrine journals. This malpractice may be further enhanced since endorsement does not imply enforcement, impairing the practice of evidence-based medicine.
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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 | 0.658 | 0.853 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.015 | 0.033 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| 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".