Efficacy of Fu’s Subcutaneous Needling in Treating Soft Tissue Pain of Knee Osteoarthritis: A Randomized Clinical Trial
Bibliographic record
Abstract
Purpose: Fu’s subcutaneous needling (FSN) is a new acupuncture technique that produces a long-lasting effect in soft-tissue injuries. In patients with degenerative knee osteoarthritis (OA), myofascial trigger points (MTrPs) are common in the lower-limb muscles. In this randomized clinical trial, we evaluated the immediate, 1-week and 2-week effectiveness of FSN therapy in the treatment of degenerative knee OA. Patients and methods: We randomly divided 32 patients with knee OA into the FSN group (mean age: 65.73 ± 6.79 years) or the transcutaneous electrical nerve stimulation (TENS) group (mean age: 62.81 ± 5.72 years). The pressure pain threshold (PPT) and tissue hardness (TH) of the muscle and tendon attachment sites, knee range of motion, and physical ability (average walking speed) were measured. The subjective pain intensity index, Western Ontario and McMaster Universities OA Index (WOMAC), and Lequesne index were used to determine the efficacy of FSN on MTrP-induced soft-tissue pain compared with that of TENS. Results: A significantly greater improvement in pain qualities in the VAS (p < 0.05) was found in the FSN group. Moreover, in muscle and tendon qualities (including PPT and TH), a significant difference in the PPT of the quadriceps muscle (p < 0.05) was also observed among the immediate treatments in the FSN group. As for the functional index questionnaire assessment, the FSN group exhibited significant improvements among the immediate, 1-week and 2-week efficacies in terms of WOMAC (p < 0.05) and Lequesne index scores (p < 0.05). Conclusion: FSN was effective in treating soft-tissue pain in degenerative knee OA in terms of alleviating pain, strengthening walking ability, and improving overall functional performance. Pain relief was the primary benefit of FSN and a significant correlation between pain relief and knee joint mobility improvement was found. Trial registration: ClinicalTrials.gov Protocol Registration and Results System (registration number: NCT04356651).
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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".