Dry needling on latent and active myofascial trigger points versus oral diclofenac in patients with knee osteoarthritis: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Latent and active myofascial trigger points (MTrPs) in knee-associated muscles may play a key role in pain management among patients with knee osteoarthritis (KOA). The aim of this study was to investigate the effect of dry needling treatment on pain intensity, disability, and range of motion (ROM) in patients with KOA. METHODS: This randomized, single-blinded, clinical trial was carried out for 6 weeks of treatment and 6-month follow-up. A total of 98 patients met the entry criteria and were randomly assigned to the dry needling latent and active myofascial trigger point (MTrPs) with the stretching group or the oral diclofenacwith the stretching group. Numeric Pain Rating Scale (NPRS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and ROM were statistically analyzed before and after treatment and at the 6-month follow-up. RESULTS: A total of 42 patients in the dry needling group (DNG) and 35 patients in the diclofenac group (DG), respectively, completed the study, and there was no significant difference in the general data between the two groups. After treatments, both the groups showed a good effect in knee pain, function, and ROM, However, the DNG showed a significantly better result than the DG. Especially in the results of the 6-month follow-up, the DNG showed much better results than the DG. CONCLUSIONS: Dry needling on latent and active MTrPs combined with stretching and oral diclofenac combined with stretching can effectively relieve pain, improve function, and restore knee ROM affected by KOA. However, the effects of dry needling and stretching are better and longer lasting than those of oral diclofenac and stretching for at least 6 months. TRIAL REGISTRATION: Registered in the Chinese Clinical Trial Registry ( www.chictr.org.cn ) in 17/11/2017 with the following code: ChiCTR-INR-17013432.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".