Acupuncture for chronic neck pain: a protocol for an updated systematic review
Bibliographic record
Abstract
BACKGROUND: This study aims to investigate the efficacy and safety of acupuncture for patients with chronic neck pain. METHODS: The MEDLINE, EMBASE, CENTRAL, CINAHL, and the Chinese Biomedical Literature Database, the China National Knowledge Infrastructure, VIP Information, and Wanfang Data databases will be searched from their inception to present. Randomised controlled trials (RCTs) of acupuncture (assessed as the sole treatment or as an adjunct treatment) for chronic neck pain will be included. The primary outcome is chronic neck pain measured by the visual analogue scale (VAS), McGill Pain Questionnaire, or short-form McGill Pain Questionnaire. The secondary outcomes will include the functional recovery, health-related quality of life, psychological improvements related to the reduction of pain, and adverse events. Two authors will perform the study selection, data extraction, and quality assessment independently. Any disagreements will be resolved through discussion with a third author. Methodological quality of the included trials will be evaluated by the Cochrane risk-of-bias criteria, and the Standards for Reporting Interventions in Controlled Trials of Acupuncture checklist will be used to assess completeness of reporting. DISCUSSION: The results of this systematic review will provide the latest evidence of the efficacy of acupuncture in treating chronic neck pain, which will benefit both practitioners and policymakers. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42015017178.
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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 | 0.058 | 0.082 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.069 | 0.010 |
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".