Acupuncture for osteoarthritic pain: an observational study in routine care
Bibliographic record
Abstract
OBJECTIVE: To investigate characteristics and outcomes of patients undergoing acupuncture treatment for osteoarthritic pain under conditions of routine care in the framework of statutory health insurance in Germany. METHODS: Patients with chronic pain due to osteoarthritis (ICD-10 diagnoses M15 to M19) treated with acupuncture as the leading form of therapy were included in an observational study. Detailed questionnaires including instruments to measure pain intensity (numerical rating scales from 0 to 10), disability (Pain Disability Index) and quality of life (SF-36) were filled in before treatment, after treatment and at 6 months. Patients suffering from osteoarthritis of the knee and hip also filled in the Western Ontario and McMaster Universities (WOMAC) Osteoarthritis Index questionnaire. RESULTS: A total of 736 patients were included in the main analysis. Seventy (10%) patients and 278 (38%) patients, respectively, suffered exclusively from primary osteoarthritis of the hip or knee, 239 (33%) from another type of osteoarthritis and 149 (20%) had more than one affected joint. On average, patients received 8.7 +/- 3.1 acupuncture treatments. Statistically significant and clinically relevant improvements were seen in all subgroups both after treatment and at 6 months in all major outcome measures. In patients with osteoarthritis of the hip, the WOMAC sum score was 47.9 +/- 20.7 at baseline, 34.8 +/- 20.0 after treatment and 33.1 +/- 22.2 at 6 months. The respective values in patients with osteoarthritis of the knee were 51.7 +/- 20.9, 34.1 +/- 23.3 and 34.6 +/- 25.1. CONCLUSIONS: In this study, patients with chronic pain due to osteoarthritis reported clinically relevant improvements after acupuncture treatment. Due to the uncontrolled design and the high proportion of patients lost to follow-up, the study findings must be interpreted cautiously.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".