Health technology assessment in traditional and complementary medicine: a scoping review of international activity and examples of acupuncture
Bibliographic record
Abstract
BACKGROUND: Traditional therapies are crucial in maintaining and improving human well-being. China's healthcare policymakers are attempting to use health technology assessment (HTA) as a decision-making supportive tool. The value assessment framework for Chinese patent medicine (CPM) has been developed and is being adopted and validated widely by research institutions. Subsequently, the healthcare decision-makers particularly hanker for the value framework of traditional non-pharmacological therapies. METHODS: To construct a practical value framework for traditional non-pharmacological therapies, a scoping review methodology was adopted to identify the evaluation domains and obstacles. A search, screening, and analysis process was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Evidence was retrieved from scientific databases and HTA agencies' websites. RESULTS: The search strategy identified 5 guidelines records and 17 acupuncture HTA reports. By synthesizing the valuable reports of CPM and acupuncture evaluation in representative countries, this study found that Mainland China was promoting the comprehensive value assessment of CPM, whereas the United Kingdom, Singapore, Canada, the United States, and Malaysia had carried out the HTA evaluation of acupuncture for various conditions among which chronic pain was the most common. UK and Singapore applied the HTA results to support acupuncture reimbursement decisions. Three domains, including safety, effectiveness, and economy, were commonly adopted. The identified biggest challenge of evaluating traditional non-pharmacological therapies is the scarce high-quality clinical evidence. CONCLUSIONS: This study identified value domains and issues of traditional therapies, and pointed out future research implications, to promote the development value framework of traditional therapies.
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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 | 0.027 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.028 | 0.032 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".