Observations on the Therapeutic Effect of Heat-sensitive Point Thunder-fire Moxibustion on Knee Osteoarthritis
Bibliographic record
Abstract
Objective To investigate the clinical efficacy of heat-sensitive point thunder-fire moxibustion in treating knee osteoarthritis(KOA).Methods One hundred and forty-eight KOA patients were randomly allocated to treatment and control groups,74 cases each.The treatment group received heat-sensitive point thunder-fire moxibustion and the control group took diclofenac sodium enteric-coated tablets.The Visual Analogue Scale(VAS) score,the Western Ontario and McMaster Universities Osteoarthritis Index(WOMAC) score and 50 yards fastest walking time were observed in the two groups before and after 30 days of treatment.The clinical therapeutic effects were compared between the two groups.Results There were statistically significant pre-/post-treatment differences in the VAS score and WOMAC subscores in the two groups(p0.01).There was a statistically significant pre-/post- treatment difference in 50 yards fastest walking time in the treatment group(P0.05).There were statistically significant post- treatment differences in the VAS score,the WOMAC score and the WOMAC pain and stiffness scores between the treatment and control groups(P0.01).There were statistically significant differences in the VAS and WOMAC scores at three months after treatment between the treatment and control groups(P0.01).The total efficacy rate was 95.9%at the end of treament and 95.6%at three months after treatment in the treatment group,and 86.1%at the end of treatment and 86.8%at three months after treatment in the control group;there were statistically significant differences between the two groups(p0.05).Conclusion Heat-sensitive point thunder-fire moxibustion is an effective way to treat knee osteoarthritis.
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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) | 0.000 | 0.000 |
| 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".