Acupuncture for chronic knee pain: a protocol for an updated systematic review
Bibliographic record
Abstract
INTRODUCTION: The aim of this study is to evaluate the efficacy and safety of acupuncture for patients with chronic knee pain. METHODS AND ANALYSIS: MEDLINE, EMBASE, CENTERAL, CINAHL and four Chinese medical databases will be searched from their inception to present. We will also manually retrieve eligible studies. Randomised controlled trials (RCTs) in which acupuncture is assessed as the sole treatment or as an adjunct treatment for chronic knee pain will be included. The primary outcome of our analysis is pain measured by the visual analogue scale (VAS), the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale or the 11-point numeric rating scale (NRS). The secondary outcomes will include the quality of life, measured by the 36-item Short-Form Health Survey (SF-36) and adverse events. Two researchers will conduct the study selection, data extraction and quality assessment independently. Any disagreement will be resolved through discussion with a third reviewer. The Cochrane risk-of-bias criteria and the Standards for Reporting Interventions in Controlled Trials of Acupuncture (STRICTA) checklist will be used to assess the methodological quality of the trials. DISSEMINATION: This systematic review will assess the current evidence on acupuncture therapy for chronic knee pain. It uses aggregated published data instead of individual patient data and does not require an ethical board review and approval. The findings will be published in a peer-reviewed journal and disseminated in conference presentations. It will provide the latest analysis of the currently available evidence for acupuncture treating chronic knee pain. TRIAL REGISTRATION NUMBER: CRD42014015514.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".