Guideline Acupuncture for low back pain: a clinical practice guideline from the Hong Kong taskforce of standardized acupuncture practice.
Bibliographic record
Abstract
OBJECTIVE: To develop a clinical practice guideline to guide the treatment of low back pain by acupuncture. METHODS: An integrative approach of systematic review of literature, clinical evidence classification, expert opinion surveying, and consensus establishing via a Delphi program was utilized during the developing process. Both evidence-based practice standards and the personalized features of acupuncture were taken into considerations. RESULTS: Based on clinical evidence and expert opinions, we developed a clinical practice guideline for the treatment of low back pain with acupuncture. These recommendations have a wide coverage spanning from Western Medicine diagnosis and Traditional Chinese Medicine syndrome differentiation, to acupuncture treatment procedures, as well as post treatment care for rehabilitation and follow-ups. The recommendations for acupuncture practice included treatment principles, therapeutic regimens, and operational procedures. The levels of evidence and strength of recommendation were rated for each procedure of practice. CONCLUSION: A clinical practice guideline for acupuncture treating low back pain was developed based on contemporary clinical evidence and experts' consensus to provide best currently agreeable practice guideline for domestic and international stakeholders.
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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 | 0.035 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".