Comparison of Massage Based on the Tensegrity Principle and Classic Massage in Treating Chronic Shoulder Pain
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to compare the clinical outcomes of classic massage to massage based on the tensegrity principle for patients with chronic idiopathic shoulder pain. METHODS: Thirty subjects with chronic shoulder pain symptoms were divided into 2 groups, 15 subjects received classic (Swedish) massage to tissues surrounding the glenohumeral joint and 15 subjects received the massage using techniques based on the tensegrity principle. The tensegrity principle is based on directing treatment to the painful area and the tissues (muscles, fascia, and ligaments) that structurally support the painful area, thus treating tissues that have direct and indirect influence on the motion segment. Both treatment groups received 10 sessions over 2 weeks, each session lasted 20 minutes. The McGill Pain Questionnaire and glenohumeral ranges of motion were measured immediately before the first massage session, on the day the therapy ended 2 weeks after therapy started, and 1 month after the last massage. RESULTS: Subjects receiving massage based on the tensegrity principle demonstrated statistically significance improvement in the passive and active ranges of flexion and abduction of the glenohumeral joint. Pain decreased in both massage groups. CONCLUSIONS: This study showed increases in passive and active ranges of motion for flexion and abduction in patients who had massage based on the tensegrity principle. For pain outcomes, both classic and tensegrity massage groups demonstrated improvement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".