STandards for Reporting Interventions in Clinical Trials Of Tuina/Massage (STRICTOTM): Extending the CONSORT statement
Bibliographic record
Abstract
OBJECTIVES: Massage is a common therapy of nonpharmacological treatments, particularly in Tuina (Chinese massage) as its most common style, detailed guidance in reporting the intervention is warranted for its evaluation and replication. Based on the CONSORT (Consolidated Standards of Reporting Trials), we aimed to develop an Extension for Tuina/Massage, namely "The STandards for Reporting Interventions in Clinical Trials Of Tuina/Massage (STRICTOTM)." METHODS: A group of professional clinicians, trialists, methodologists, developers of reporting guidelines, epidemiologists, statisticians, and editors has developed this STRICTOTM checklist through a standard methodology process recommended by the EQUATOR (Enhancing the QUAlity and Transparency of Health Research) Network, including prospective registration, literature review, draft of the initial items, three rounds of the Delphi survey, consensus meeting, pilot test, and finalization of the guideline. RESULTS: A checklist of seven items (namely Tuina/Massage rationale, details of Tuina/Massage, intervention regimen, other components of the intervention, Tuina/Massage provider background, control or comparator interventions, and precaution measures), and 16 subitems were developed. Explanations and examples (E&E) for each item are also provided. CONCLUSIONS: The working group hopes that the STRICTOTM, in conjunction with both the CONSORT statement and extension for nonpharmacologic treatment, can improve the reporting quality and transparency of Tuina/Massage clinical research.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.779 | 0.829 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.011 | 0.014 |
| Research integrity | 0.020 | 0.029 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".