A Novel Massage Therapy Technique for Management of Chronic Cervical Pain: A Case Series
Bibliographic record
Abstract
BACKGROUND: Neck pain is a generalized condition resulting from a complex etiology with presentation of a wide variety of symptoms. Neck pain is most often accompanied by decreased range of motion (ROM), muscle and joint stiffness, and limitations in functional capabilities. This condition may result in significant personal and societal burden. PURPOSE: We evaluated the effectiveness of a novel massage therapy intervention by following the treatment regimen and outcomes of two patients experiencing chronic neck pain. PARTICIPANTS: Two patients (46 and 53 years old) experienced chronic (>5 years) neck pain. Both patients reported pain, limited ROM, and muscle and joint stiffness. Additionally, the first patient reported a lack of sleep, and both patients stated their pain interfered with their quality of life and activities of daily living. INTERVENTION: Patients received the Integrative Muscular Movement Technique (IMMT) intervention approximately twice a week for a total of eight treatments, each approximately 20 minutes in duration. RESULTS: Both patients experienced a reduction in pain and an increase in cervical ROM in flexion, extension, rotation, and sidebending. The first patient also reported an increased ability to sleep. Both patients reported an increased ability to perform activities of daily living, including work-related responsibilities. CONCLUSIONS: For the two patients included in this report, therapist observations and patient reports indicate that inclusion of the IMMT treatment in a treatment regimen for chronic neck pain may lead to decreased pain and increased cervical ROM. These positive effects of the IMMT intervention may have a role in enhancing functional outcomes of these patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".