Reporting guidelines for traditional Chinese medicine could be improved: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: The aim of this study is to identify available reporting guidelines for traditional Chinese medicine (TCM), delineate their fundamental characteristics, assess the scientific rigor of their development process, and evaluate their dissemination. STUDY DESIGN AND SETTING: A search was conducted in Medline (via PubMed), China National Knowledge Infrastructure (CNKI), SinoMed, WANFANG DATA, and the EQUATOR Network to identify TCM reporting guidelines. A preprepared Excel database was used to extract information on the basic characteristics, development process, and dissemination information. The development process quality of TCM reporting guidelines was assessed by evaluating their compliance with the Guidance for Developers of Health Research Reporting Guidelines (GDHRRG). The extent of dissemination of these guidelines was analyzed by examining the number of citations received. RESULTS: A total of 26 reporting guidelines for TCM were obtained from 20 academic journals, with 61.5% of them published in English journals. Among the guidelines, 14 (53.8%) were registered in the EQUATOR Network. On average, the compliance rate of GDHRRG guidelines was reported to be 63.3% ranging from 22.2% to 94.4%. Three steps showed poor compliance, namely guideline endorsement (23.1%), translated guidelines (19.2%), and developing a publication strategy (19.2%). Furthermore, the compliance rate of GDHRRG guidelines published in English journals was higher than that in Chinese journals. In terms of the dissemination, 15.4% of the guidelines had been cited over 100 times, while 73.1% had been cited less than 50 times. CONCLUSION: The development of TCM reporting guidelines still has limitations in terms of regarding scientific rigor and follow-up dissemination. Therefore, it is important to ensure adherence to the scientific process in the development of TCM reporting guidelines and to strengthen their promotion, dissemination, and implementation.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".