Clinical Practice Guideline on Acupuncture and Moxibustion: Adult Major Depressive Disorder (Mild-Moderate Degree): Determination of clinical questions
Bibliographic record
Abstract
Determining clinical questions is fundamental to the development of clinical practice guidelines (CPGs), which bridges the initial phases and the final recommendations. It is essential for evidence retrieval and the formulation of recommendations. The scientific rigor and precision in determination of clinical questions directly influence the future implementation and applicability of guidelines. In 2020, the World Federation of Acupuncture-Moxibustion Societies initiated the project of clinical practice guideline on acupuncture and moxibustion for adult major depressive disorder (mild-moderate degree) to address clinical and medical decision-making issues in acupuncture treatment for adult mild to moderate major depressive disorder. This CPG provides systematic recommendations based on clinical evidence, patient values, and other factors, aiding decision-makers, clinicians, and patients in selecting appropriate interventions. This paper discusses and analyzes the determination process of clinical questions, and the related issues during the development of this guideline, aiming to provide a reference for determining clinical questions and developing CPGs in the field of acupuncture and exploring more scientific tools and methods for determining clinical questions in future CPGs.
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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 | 0.021 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".