The Effect of Sa-am acupuncture on Knee Osteoarthritis
Bibliographic record
Abstract
Objective: Acupuncture has been widely used throughout the world for the treatment of knee osteoarthritis (knee OA). This study investigated whether acupuncture, particularly Sa-am acupuncture, could be effective in relieving pain and improving the symptoms of knee OA. Method: This study was conducted as a prospective, randomized, controlled, and patient- and investigator- blinded clinical trial. Forty volunteers with knee OA participated in the study. All participants were screened through an inclusion and exclusion criteria. Thirty four participants completed the clinical trial. In total, forty subjects were randomly selected to receive Sa-am acupuncture. Eight sessions of acupuncture were given at the contralateral side of the problematic knee for 4 weeks. Korean translation of Western Ontario and McMaster Universities Osteoarthritis Index (KWOMAC) scores were measured twice: at the beginning and end of the clinical trial period. Both the Patient Global Assessment and physical health scores based on the 36-Item Short-Form Health Survey were also used to measure the results. Results: Compared to the pre-trial scores, the Sa-am acupuncture group (n=34) showed a significant decrease in KWOMAC total scores according to a paired t -test. The Sa-am acupuncture group also showed significant improvement in the Patient Global Assessment when compared to the pre-trial. Conclusions: Sa-am acupuncture for knee OA resulted in an improved KWOMAC total score. This was mostly driven by the function component score that was greatly affected by acupuncture. However, further studies with expanded designs are needed to solidify this finding with scientific rigor.
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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 | 0.001 |
| 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) | 0.002 | 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".