Therapeutic effects oflow-frequency phonophoresis with a Chinese herbal medicine versus sodium diclofenac for treatment of knee osteoarthritis: a double-blind, randomized, placebo-controlled clinical trial
Bibliographic record
Abstract
OBJECTIVE: To evaluate the therapeutic effects of low-frequency phonophoresis with a Chinese herbal medicine (CHM) compared with sodium diclofenac (SD) for knee osteoarthritis (KOA). METHODS: In this double-blind, randomized, placebo-controlled trial, 100 KOA patients were assigned randomly to a placebo group, a CHM group, or SD group. Low-frequency phonophoresis was used to improve the efficiency of drug delivery. Pain at rest [using a visual analog scale (VAS)], pain on movement (VAS), and range of motion (degrees) in the three groups were evaluated using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMACAI) scores. Safety assessments comprised emergency adverse events, as well as laboratory tests of blood biochemistry, creatinine, blood urea nitrogen, alanine aminotransferase and aspartate aminotransferase. RESULTS: Significant improvements were found after treatment in all outcome measures except stiffness and range of motion in patients in the CHMP group and SDP group (P < 0.05). No significant differences in all outcome measures were found between the CHMP group and SDP group. CONCLUSION: CHMP and SDP can show good therapeutic effects for KOA in terms of relieving pain and improving physical function.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".