Validating a Nonacupoint Sham Control for Laser Treatment of Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: The goals of the present study were to evaluate the effect and safety of combined 10.6 microm and 650 nm laser acupuncture-moxibustion on patients with knee osteoarthritis (OA) and to validate a nonacupoint sham control for assessing the effect of point specificity on the treatment. MATERIALS AND METHODS: A randomized, sham-controlled clinical trial was conducted in an outpatient clinical setting on patients with knee OA (n = 40). Laser irradiation was performed on acupoint Dubi (ST35) and a sham point three times a week for 4 wk. Outcome measurements were performed at baseline and at wk 2 and 4 using Western Ontario and McMaster Universities' Osteoarthritis Index (WOMAC). RESULTS: At the 2-wk assessment, i.e., after 6 treatments, improvement in the WOMAC pain score of the acupoint group was significantly greater than that of the control group (49.21% vs. 11.99%, respectively; p = 0.021). However, there were no significant differences between the two groups in the WOMAC physical function score (p = 0.129) or joint stiffness score (p = 0.705). No side effects were found during the trial. CONCLUSIONS: Combined 10.6-mum-650-nm laser acupuncture-moxibustion on acupoint ST35 is safe to use and was effective after 2-wk treatment, but not at the 4-wk assessment, in relieving knee OA pain compared to a nonacupoint sham control. A larger clinical trial to verify our findings is warranted.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".