Superficial Dry Needling and Active Stretching in the Treatment of Myofascial Pain – a Randomised Controlled Trial
Bibliographic record
Abstract
UNLABELLED: A pragmatic, single blind, randomised, controlled trial was conducted to test the hypothesis that superficial dry needling (SDN) together with active stretching is more effective than stretching alone, or no treatment, in deactivating trigger points (TrPs) and reducing myofascial pain. Forty patients with musculoskeletal pain, referred by GPs for physiotherapy, fulfilled inclusion/ exclusion criteria for active TrPs. Subjects were randomised into three groups: group 1(n = 14) received superficial dry needling (SDN) and active stretching exercises (G1); group 2 (n = 13) received stretching exercises alone (G2); and group 3 (n = 13) were no treatment controls (G3). During the three-week intervention period for G1 and G2, the number of treatments varied according to the severity of the condition and subject/clinician availability. Assessment was carried out pre-intervention (M1, post-intervention (M2), and at a three-week follow up (M3). Outcome measures were the Short Form McGill Pain Questionnaire (SFMPQ) and Pressure Pain Threshold (PPT) of the primary TrP, using a Fischer algometer. Ninety-one per cent of assessments were blind to grouping. At M2 there were no significant inter-group differences, but at M3, G1 demonstrated significantly improved SFMPQ versus G3 (p = 0 .043) and significantly improved PPT versus G2 (p = 0 .011). There were no differences between G2 and G3. The mean PPT and SFMPQ scores correlated significantly in G1 only, though no significant inter-group differences were demonstrated. Numbers of patients requiring further treatment following the trial were: 6 (G1); 12 (G2); 9 (G3). CONCLUSION: SDN followed by active stretching is more effective than stretching alone in deactivating TrPs (reducing their sensitivity to pressure), and more effective than no treatment in reducing subjective pain. Stretching without prior deactivation may increase TrP sensitivity.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".