The reporting checklist for Chinese patent medicine guidelines: RIGHT for CPM
Bibliographic record
Abstract
Existing reporting checklists lack the necessary level of detail and comprehensiveness to be used in guidelines on Chinese patent medicines (CPM). This study aims to develop a reporting guidance for CPM guidelines based on the Reporting Items of Practice Guidelines in Healthcare (RIGHT) statement. We extracted information from CPM guidelines, existing reporting standards for traditional Chinese medicine (TCM), and the RIGHT statement and its extensions to form the initial pool of reporting items for CPM guidelines. Seventeen experts from diverse disciplines participated in two rounds of Delphi process to refine and clarify the items. Finally, 18 authoritative consultants in the field of TCM and reporting guidelines reviewed and approved the RIGHT for CPM checklist. We added 16 new items and modified two items of the original RIGHT statement to form the RIGHT for CPM checklist, which contains 51 items grouped into seven sections and 23 topics. The new and revised items are distributed across four sections (Basic information, Background, Evidence, and Recommendations) and seven topics: title/subtitle (one new and one revised item), Registration information (one new item), Brief description of the health problem (four new items), Guideline development groups (one revised item), Health care questions (two new items), Recommendations (two new items), and Rationale/explanation for recommendations (six new items). The RIGHT for CPM checklist is committed to providing users with guidance for detailed, comprehensive and transparent reporting, and help practitioners better understand and implement CPM guidelines.
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 | 0.299 | 0.527 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".