Does Deep Cervical Flexor Muscle Training Affect Pain Pressure Thresholds of Myofascial Trigger Points in Patients with Chronic Neck Pain? A Prospective Randomized Controlled Trial
Bibliographic record
Abstract
Background. We need to understand more about how DNF performs in different contexts and whether it affects the pain threshold over myofascial trigger points (MTrPs). Purpose. The objectives were to investigate the effect of neck muscles training on disability and pain and on pain threshold over MTrPs in people with chronic neck pain. Methods. Patients with chronic neck pain were eligible for participation with a Neck Disability Index (NDI) score of over 5/50 and having at least one MTrP on either levator scapulae, upper trapezoid, or splenius capitis muscle. Patients were randomly assigned into either DNF training, superficial neck muscle exercise, or advice group. Generalized linear model (GLM) was used to detect differences in treatment groups over time. Results. Out of 67 participants, 60 (47 females, mean age: 39.45 ± 12.67) completed the study. Neck disability and neck pain were improved over time between and within groups ( p<0.05 ). However, no differences were found within and between the therapeutic groups ( p<0.05 ) in the tested muscles’ PPTs and in cervicothoracic angle over a 7-week period. Conclusion. All three groups improved over time. This infers that the pain pathways involved in the neck pain relief are not those involved in pain threshold.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".