A cross-sectional study of the endorsement proportion of reporting guidelines in 1039 Chinese medical journals
Bibliographic record
Abstract
Abstract Background Reporting quality is a critical issue in health sciences. Adopting the reporting guidelines has been approved to be an effective way of enhancing the reporting quality and transparency of clinical research. In 2012, we found that only 7 (7/1221, 0.6%) journals adopted the Consolidated Standards of Reporting Trials (CONSORT) statement in China. The aim of the study was to know the implementation status of CONSORT and other reporting guidelines about clinical studies in China. Methods A cross-sectional bibliometric study was conducted. Eight medical databases were systematically searched, and 1039 medical journals published in mainland China, Hong Kong, Macau, and Taiwan were included. The basic characteristics, including subject, language, publication place, journal-indexed databases, and journal impact factors were extracted. The endorsement of reporting guidelines was assessed by a modified 5-level evaluation tool, namely i) positive active, ii) positive weak, iii) passive moderate, iv) passive weak and v) none. Results Among included journals, 24.1% endorsed CONSORT, and 0.8% endorsed CONSORT extensions. For STROBE (STrengthening the Reporting of Observational Studies in Epidemiology), PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), STARD (An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies), CARE (CAse REport guidelines), the endorsement proportion were 17.2, 16.6, 16.4, and 14.8% respectively. The endorsement proportion for SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials), TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis), AGREE (Appraisal of Guidelines, Research, and Evaluation), and RIGHT (Reporting Items for Practice Guidelines in Healthcare) were below 0.7%. Conclusions Our results showed that the implementation of reporting guidelines was low. We suggest the following initiatives including i) enhancing the level of journal endorsement for reporting guidelines; ii) strengthening the collaboration among authors, reviewers, editors, and other stakeholders; iii) providing training courses for stakeholders; iv) establishing bases for reporting guidelines network in China; v) adopting the endorsement of reporting guidelines in the policies of the China Periodicals Association (CPA); vi) promoting Chinese medical journals into the international evaluation system and publish in English.
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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 | 0.055 | 0.182 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".