The Effects of Massage on Pain, Stiffness, and Fatigue Levels Associated with Ankylosing Spondylitis: A Case Study
Bibliographic record
Abstract
OBJECTIVES: To study the effects of massage on pain, stiffness, and fatigue in a patient recently diagnosed with ankylosing spondylitis (AS). METHODS: A 47-year-old woman with AS diagnosed 11 months earlier received 7 massages across a 28-day period. Her pain, stiffness, and fatigue were recorded using visual analogue scales daily during the study period. Spinal mobility was measured at each massage session with finger-to-floor measurements for both forward and lateral flexion. The client also used a daily journal to supply pertinent information on quality of life. RESULTS: Improvement was recorded in all dependent variables, with stiffness intensity showing the greatest improvement, to a final value of 0.75 from an initial value of 5. Duration of stiffness also showed improvement, to a final value of 1.2 from an initial value of 3.5. Improvement was also found in general pain (final value: 1; initial value: 4), fatigue (final value: 1.5; initial value 5), and forward and lateral flexion (forward flexion distance-final: 4 inches; initial: 6 inches; lateral flexion, left distance: final, 16.5 inches; initial, 21 inches; right distance: final, 16.5 inches; initial, 20.5 inches). CONCLUSIONS: Massage shows promise as a treatment for symptoms associated with AS. Further study is needed to validate these effects and to determine the feasibility of massage as an adjunct to standard care for AS patients with mild-to-moderate symptoms of AS.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".