Effectiveness of Myofacial Release versus Proprioceptive Neuro Facilitation Technique on Pain and Range of Motion in Patients with Cervicogenic Headache
Bibliographic record
Abstract
Background: Cervicogenic headache is a prevalent condition characterized by headaches caused by cervical musculoskeletal impairments. The effectiveness of Myofascial Release (MFR) and Proprioceptive Neuromuscular Facilitation (PNF) in managing this condition has been explored, with varying outcomes on pain intensity, cervical range of motion (ROM), and functional disability. Objective: To compare the efficacy of MFR and PNF techniques in reducing pain and improving ROM in patients with cervicogenic headache. Methods: This randomized control trial included 30 female participants diagnosed with cervicogenic headache. Participants were randomly assigned to receive either MFR or PNF treatments over a period of 4 weeks, with sessions conducted twice weekly. Outcome measures included pain intensity assessed by the Visual Analog Scale (VAS), cervical ROM measured using a universal goniometer, and functional disability evaluated through the Neck Disability Index (NDI). Data were analyzed using SPSS version 25, employing t-tests for within-group comparisons and ANOVA for between-group analyses. Results: Both MFR and PNF groups showed significant improvements post-treatment. The MFR group demonstrated a reduction in VAS scores from 6.73 ± 0.593 to 4.26 ± 0.703 (p<0.001), and the PNF group from 6.73 ± 0.593 to 5.26 ± 0.432 (p<0.001). Cervical ROM and NDI scores also significantly improved in both groups. Comparative analysis revealed MFR to be more effective in enhancing cervical rotation (MFR: 70.13 ± 3.020 to 80.20 ± 2.840; PNF: 60.33 ± 2.690 to 64.06 ± 2.548; p<0.05) and reducing NDI scores (MFR: 48.33 ± 5.56 to 17.53 ± 5.46; PNF: 10.00 ± 2.267 to 8.93 ± 1.667; p<0.001). Conclusion: Both MFR and PNF are effective in treating cervicogenic headache, significantly reducing pain intensity and improving cervical ROM and functional disability. MFR, however, exhibited a superior efficacy in enhancing cervical rotation and reducing NDI scores, suggesting it may offer additional benefits in the management of cervicogenic headache.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".