Effectiveness guidance document (EGD) for acupuncture research - a consensus document for conducting trials
Bibliographic record
Abstract
BACKGROUND: There is a need for more Comparative Effectiveness Research (CER) to strengthen the evidence base for clinical and policy decision-making. Effectiveness Guidance Documents (EGD) are targeted to clinical researchers. The aim of this EGD is to provide specific recommendations for the design of prospective acupuncture studies to support optimal use of resources for generating evidence that will inform stakeholder decision-making. METHODS: Document development based on multiple systematic consensus procedures (written Delphi rounds, interactive consensus workshop, international expert review). To balance aspects of internal and external validity, multiple stakeholders including patients, clinicians and payers were involved. RESULTS: Recommendations focused mainly on randomized studies and were developed for the following areas: overall research strategy, treatment protocol, expertise and setting, outcomes, study design and statistical analyses, economic evaluation, and publication. CONCLUSION: The present EGD, based on an international consensus developed with multiple stakeholder involvement, provides the first systematic methodological guidance for future CER on acupuncture.
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 | 0.510 | 0.631 |
| Meta-epidemiology (narrow) | 0.005 | 0.008 |
| Meta-epidemiology (broad) | 0.014 | 0.020 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.021 | 0.016 |
| Research integrity | 0.063 | 0.042 |
| Insufficient payload (model declined to judge) | 0.017 | 0.016 |
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".