Integrating Acupuncture With Exercise-Based Physical Therapy for Knee Osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis is a chronic disease associated with significant morbidity and economic cost. The efficacy of acupuncture in addition to traditional physical therapy has received little study. OBJECTIVE: The objective of this study was to compare the efficacy and safety of integrating a standardized true acupuncture protocol versus nonpenetrating acupuncture into exercise-based physical therapy (EPT). METHODS: This was a randomized, double-blind, controlled trial at 3 physical therapy centers in Philadelphia, PA. We studied 214 patients (66% African Americans) with at least 6 months of chronic knee pain and x-ray-confirmed Kellgren scores of 2 or 3. Patients received 12 sessions of acupuncture directly following EPT over 6 to 12 weeks. Acupuncture was performed at the same 9 points dictated by the traditional Chinese "Bi" syndrome approach to knee pain, using either standard needles or Streitberger non-skin-puncturing needles. The primary outcome was the proportion of patients with at least a 36% improvement in Western Ontario and McMaster Universities Osteoarthritis Index score at 12 weeks. RESULTS: Both treatment groups showed improvement from combined therapy with no difference between true (31.6%) and nonpenetrating acupuncture (30.3%) in Western Ontario and McMaster Universities Osteoarthritis Index response rate (P = 0.5) or report of minor adverse events. A multivariable logistic regression prediction model identified an association between a positive expectation of relief from acupuncture and reported improvement. No differences were noted by race, sex, or age. CONCLUSIONS: Puncturing acupuncture needles did not perform any better than nonpuncturing needles integrated with EPT. Whether EPT, acupuncture, or other factors accounted for any improvement noted in both groups could not be determined in this study. Expectation for relief was a predictor of reported benefit.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".