Reporting quality of economic evaluations of the negotiated Traditional Chinese Medicines in national reimbursement drug list of China: A systematic review
Bibliographic record
Abstract
Background: Traditional Medicine (TM) has a wide uptake in most countries. In China, Traditional Chinese Medicine (TCM) is a common kind of primary health because of its beneficial effects. This review aimed to appraise the publication reporting quality of economic evaluations for selective TCM in the National Reimbursement Drug List (NRDL), Version 2020, based on the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement. Methods: Electronic databases were searched for economic evaluation that supported the TCM negotiations in NRDL (2020 version) published from 2001 to 2021, including PubMed, Web of Science, Embase, CNKI, WanFang, and SinoMed. The CHEERS statement was used to appraise the reporting quality of included TCM economic evaluations. Results: A total of 360 articles were retrieved, but only 38 economic evaluations met the inclusion criteria. None of the articles reported all items in the CHEERS checklist. The mean score of included articles is low at 10.93±2.62, with an average scoring rate of 51.31±10.53%. The least reported items included: "Characterizing heterogeneity," "Conflicts of interest", "Discount rate," and "Study perspective," with a reporting rate of 0.00%, 5.26%, 7.89%, and 15.79%, respectively. Conclusion: An upward trend occurred in the quantity and quality of the economic evaluation publications of TCM in China. TCM economic evaluations are still at an early stage, with an urgent need for improving reporting quality. It may result from research experiences or different ideas between TCM and Western Medicine. Adhering to reporting guidelines like CHEERS and educating economic evaluation investigators can improve TCM economic evaluations' reporting quality.
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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 | 0.133 | 0.410 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".