Pilot and feasibility trials in traditional Chinese medicine: a literature survey
Bibliographic record
Abstract
Abstract Background The guidelines for pilot and feasibility studies were published in 2016. Little is known about the guideline adherence of TCM (Traditional Chinese Medicine) pilot trials or whether the guidelines can significantly enhance the quality of implementation and reporting of TCM pilot trials. We aimed to investigate the guideline adherence, assess the impact of guidelines on TCM pilot trials, and discuss potentially undetected challenges for TCM pilot trials, by conducting a liteature survey.Methods We systematically searched MEDLINE, EMBASE and CNKI to retreive TCM pilot trials. We randomly chose 50 pilot trials from the eligible studies for analyses. The CONSORT extension to pilot and feasibility studies was used as a framework to assess the methodology and reporting quality of the studies.Results The included stuides had a guideline adherence level ranging from 4% to 96%, where the lowest adherence was found in the item 6c (prespecified criteria used to judge progression to future definitive trial). The guidance published in 2016 seemed to exert minimal effect on guideline adherence in TCM pilot trials. The unidentified issues related to TCM pilot trials from the guidelines included blinding, lack of standard formula of interventions, difficulty in comparison for effect assessment of interventions, and difficulty in bias control.Conclusions The current practice in TCM pilot trials requried substantial improvement in the literature. Further endeavors are needed for training and dissemination of guideline adherence, and development of more detailed methodology in the field of TCM pilot trials.
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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 | 0.275 | 0.499 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".