Treatment of Myofascial Shoulder Pain in The Spinal Cord Injured Population Using Static Magnetic Fields: A Case Series
Bibliographic record
Abstract
OBJECTIVE: Magnetic therapy has been used in the treatment of a wide variety of chronic pain syndromes. It has not been studied in the treatment of myofascial shoulder pain in persons with spinal cord injury (SCI). Because this type of pain is commonly refractory to traditional therapy, alternative treatments often are considered. The primary objective is to determine whether myofascial shoulder pain in persons with SCI can be temporarily ameliorated with static magnetic fields. DESIGN: Case series. SETTING: Clinic of a university hospital system. PARTICIPANTS: A volunteer sample of 8 participants with SCI; 3 women, 5 men; mean age = 45 years; mean duration of injury = 12.3 years. INTERVENTIONS: Placement of a commercially available magnet with a static magnetic field of 500 gauss on the affected shoulder for 1 hour. MAIN OUTCOME MEASURES: Pretreatment and posttreatment scores on the short-form McGill Pain Questionnaire and pressure algometry were compared. RESULTS: The short-form McGill Pain Questionnaire descriptors demonstrated significant decreases: stabbing, 0.75 +/- 0.71 (P < 0.02); sharp, 0.50 +/- 0.53 (P < 0.033); and tender, 0.88 +/- 0.83 (P < 0.021). They also demonstrated a significant decrease in the present pain intensity of 0.63 +/- 0.52 (P < 0.011). Participants demonstrated a nonsignificant decrease of 0.813 +/- 0.998 (P < 0.55) on the visual analog scale. Pressure algometry was nonsignificant with a difference of 0.062 +/- 1.17 (P < 0.885). CONCLUSION: Static magnetic fields may decrease the sensory dimensions and intensity of myofascial shoulder pain in persons with SCI.
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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 | 0.003 |
| 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 | 0.003 | 0.001 |
| 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".