The The Effects of Massage Therapy on a Patient with Migraines and Cervical Spondylosis: a Case Report
Bibliographic record
Abstract
BACKGROUND: Migraines involve moderate-to-severe neck and face pain that lasts four to 72 hours, and are followed by fatigue and stiffness. Migraines are treated using medications, massage therapy (MT), and non-pharmacological alternatives. Cervical spondylosis (CS) is characterized by degeneration of the intervertebral discs, neck pain, and involvement of soft tissues in the cervical area. CS is treated using medications and manual therapy, including MT. OBJECTIVE: To determine the effects of MT on cervical range of motion and daily function in a patient with migraines and CS. CASE PRESENTATION: The patient was an active 56-year-old female diagnosed with migraines and CS. Initial evaluation included cervical range of motion (ROM), goniometry, reflexes, myotomes, dermatomes, local sensation testing and orthopedic tests. Assessment was followed by five MT treatments. Swedish massage, myofascial trigger point release, and proprioceptive neuromuscular facilitation (PNF) stretching were applied to the back, neck, head, and face. The Headache Disability Index (HDI) was administered on the initial and final visits to evaluate patient function. Cervical ROM was measured pre- and posttreatment using a universal goniometer. Treatment was conducted by a second-year MT student at the MacEwan Massage Therapy Teaching Clinic in Edmonton, Alberta. RESULTS: All cervical ranges of motion improved. The Headache Disability Index score decreased, but was not considered significant. The patient reported decreased stiffness in the upper back and shoulders, reduced migraines, and better sleeping patterns after the MT intervention. CONCLUSION: MT was effective in increasing cervical ROM, but had no significant effect on daily function. Further research is warranted on effects of MT on CS and migraines.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".