Effectiveness of Neck Myofascial Release Techniques and Exercise Therapy on Pain Intensity and Disability in Patients with Chronic Tension-Type Headache
Bibliographic record
Abstract
PURPOSE: Tension type headache (TTH) is one of the most prevalent types of headache. TTH is classified as episodic if it occurs on less than 15 days a month and as chronic if it occurs more often. Tension, anxiety and depression are some etiological factors for TTH which leads to work efficiency reduction. Today the interest in non-pharmacological methods is increasing; massage is one of these approaches which has no side effects. Aim of this study was to investigate the effects of neck Myofascial Release (MFR) techniques and exercise therapy on pain intensity and disability in patients with chronic tension-type headache.METHODS: This randomized clinical trial study was investigated on 30 females suffering from TTH. Participants were randomly assigned into two equal groups (n=15). The MFR group received neck MFR massage and exercise therapy four times a week for 3 weeks, each session lasting 45 minutes. Control group had no intervention. Outcomes were headache intensity and disability measured by numerical rating scale (NRS) and headache disability index (HDI), respectively. Data was analysed through independent and pair t-test.RESULTS: Between group comparison showed significant improvement of headache intensity and disability rate in MFR group (p<0.05) than control group (p=0.000).DISCUSSION: This study provides evidences that MFR technique and exercise therapy have significant effect on patients with TTH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".