Clinical decisions in the use of acupuncture as an adjunctive therapy for osteoarthritis of the knee.
Bibliographic record
Abstract
OBJECTIVE: To determine whether demographic, medical history, or arthritis assessment data may influence outcome and rate of decay for patients with osteoarthritis treated with acupuncture. DESIGN: Seventy-three persons with symptomatic osteoarthritis of the knee were recruited for this randomized controlled trial. Both treatment and crossover control groups received acupuncture treatments twice weekly for 8 weeks. Patients self-scored on the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and the Lequesne Algofunctional Index at baseline and 4, 8, and 12 weeks. Sample size for this outcome analysis was 60 patients at 4 weeks, 58 at 8 weeks, and 52 at 12 weeks. RESULTS: Patients' scores on both indexes improved at 4, 8, and 12 weeks. Scores were stable regardless of the baseline severity of the osteoarthritis. Despite some decay in outcomes at week 12, measures were significantly improved over baseline. With WOMAC scores partitioned into equal quartiles, a strong effect on outcome was apparent at 12 weeks (4 weeks after treatment) related to initial WOMAC scores. The group with the least disability and pain rebounded to original levels to a greater degree than did those who initially were more disabled. The more disabled groups retained the benefits of acupuncture treatment through the 12-week period. CONCLUSION: Acupuncture for patients with osteoarthritis of the knee may best be used early in the treatment plan, with a methodical decrease in frequency in treatment once the acute treatment period is completed to avoid a rebound effect. Demographic and medical history data were not mediating variables.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".