Acupuncture for Neck Disorders
Bibliographic record
Abstract
STUDY DESIGN: Systematic review. OBJECTIVE: To determine the effects of acupuncture for individuals with neck pain. SUMMARY OF BACKGROUND DATA: Neck pain is one of the 3 most frequently reported complaints of the musculoskeletal system. Treatments for neck pain are varied, as are the perceptions of benefits. METHODS.: We searched CENTRAL (2006, issue 1) and MEDLINE, EMBASE, MANTIS, Cumulative Index to Nursing and Allied Health Literature from their beginning to February 2006. We searched reference lists and the acupuncture database TCMLARS in China. Any published trials using randomized (RCT) or quasi-randomized (quasi-RCT) assignment to the intervention groups, either in full text or abstract form, were included. RESULTS: We found 10 trials that examined acupuncture treatments for chronic neck pain. Overall, methodologic quality had a mean of 2.3 of 5 on the Jadad scale. For chronic mechanical neck disorders, there was moderate evidence that acupuncture was more effective for pain relief than some types of sham controls, measured immediately posttreatment. There was moderate evidence that acupuncture was more effective than inactive, sham treatments measured immediately posttreatment, and at short-term follow-up (pooled standardized mean difference, -0.37; 95% confidence interval, -0.61 to -0.12). There was limited evidence that acupuncture was more effective than massage at short-term follow-up. For chronic neck disorders with radicular symptoms, there was moderate evidence that acupuncture was more effective than a wait-list control at short-term follow-up. CONCLUSIONS: There is moderate evidence that acupuncture relieves pain better than some sham treatments, measured at the end of the treatment. There is moderate evidence that those who received acupuncture reported less pain at short-term follow-up than those on a waiting list. There is also moderate evidence that acupuncture is more effective than inactive treatments for relieving pain posttreatment, and this is maintained at short-term follow-up.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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; 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".