Therapeutic analysis of laser moxibustion for different KL graded knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Our previous studies showed that laser moxibustion may be effective in alleviating the symptoms of knee osteoarthritis. However, the therapeutic effect in patients with different Kellgren-Lawrence (KL) grades is still unclear. We aimed to compare the efficacy of laser moxibustion in the treatment of knee osteoarthritis with different KL grades. METHODS: A total of 392 symptomatic KOA patients with different KL grades were randomly assigned to the laser treatment or sham laser control group (1:1). The patients received laser moxibustion treatment or sham treatment 3 times a week for 4 weeks. Outcomes were measured using the Western Ontario and McMaster Universities Arthritis Index (WOMAC) scores and Visual Analog Scale (VAS) scores, and the primary outcome measurement was the change in WOMAC pain scores from baseline to week 4. RESULTS: Among 392 randomized participants, 364 (92.86%) completed the trial. Participants with KL grades 2, 3, and 4 had significantly higher pain, functional, and total WOMAC scores than those with KL grade 1. Spearman correlation test results showed a positive correlation between KL grade and WOMAC pain, function, stiffness scores, and WOMAC total scores. That is, the higher the KL grade, the higher the WOMAC pain, function, stiffness, and WOMAC total scores. After 4 weeks of treatment, patients with KL grades 2 and 3 had significantly higher improvement scores in pain, function, and total scores than those with KL grade 1, whereas those with KL grade 2 had significantly higher improvement scores in stiffness than those with KL grade 1. Patients with KL grade 4 showed no significant effects after laser moxibustion treatment. CONCLUSION: Laser moxibustion is effective for pain reduction and functional improvement in the treatment of KOA with KL grades 2 and 3.
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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 | 0.001 | 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 | 0.001 |
| 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".