[Therapeutic effect and mechanism of silver needle heat conduction therapy combined with loxoprofen sodium patch in patients with knee osteoarthritis].
Bibliographic record
Abstract
OBJECTIVE: To observe the clinical efficacy of silver needle heat conduction therapy combined with loxoprofen sodium patch in the treatment of knee osteoarthritis (KOA). METHODS: A total of ninety-two patients with KOA were randomly and equally divided into loxoprofen sodium group and silver needle heat conduction therapy + loxoprofen sodium (combination) group, with 46 cases in each group. Patients of the combination group were treated with silver needle heat conduction therapy combined with loxoprofen sodium patch, while those of the loxoprofen sodium group were treated with loxoprofen sodium patch. The treatment was conducted for 4 weeks. The Western Ontario McMaster Universities Osteoarthritis Index (WOMAC), bone metabolism index ［including bone gla protein (BGP), bone-specific alkaline phosphatase (BALP), tartrate resistant acid phosphatase isomer (TRACP)-5b］, and inflammation factors ［including the tumor necrosis factor-α (TNF-α), transforming growth factor-β (TGF-β), interleukin-1β (IL-1β)］ were observed before and after treatment. The therapeutic effect was assessed after the treatment. RESULTS: <0.05). CONCLUSION: Silver needle heat conduction therapy combined with loxoprofen sodium can effectively treat KOA, its mechanism may be related to alleviating inflammation and improving bone metabolism.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".