Physical or Occupational Therapy Use in Systemic Sclerosis: A Scleroderma Patient-centered Intervention Network Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) is characterized by significant disability because of musculoskeletal involvement. Physical and occupational therapy (PT/OT) have been suggested to improve function. However, the rate of PT/OT use has been shown to be low in SSc. We aimed to identify demographic, medical, and psychological variables associated with PT/OT use in SSc. METHODS: Participants were patients with SSc enrolled in the Scleroderma Patient-centered Intervention Network (SPIN) Cohort. We determined the rate and indication of PT/OT use in the 3 months prior to enrollment. Multivariable logistic regression was used to identify variables independently associated with PT/OT use. RESULTS: Of the 1627 patients with SSc included in the analysis, 23% used PT/OT in the preceding 3 months. PT/OT use was independently associated with higher education (OR 1.08, 95% CI 1.04-1.12), having moderately severe small joint contractures (OR 2.09, 95% CI 1.45-3.03), severe large joint contractures (OR 2.33, 95% CI 1.14-4.74), fewer digital ulcerations (OR 0.70, 95% CI 0.51-0.95), and higher disability (OR 1.54, 95% CI 1.18-2.02) and pain scores (OR 1.04, 95% CI 1.02-1.06). The highest rate of PT/OT use was reported in France (43%) and the lowest, in the United States (17%). CONCLUSION: Despite the potential of PT/OT interventions to improve function, < 1 in 4 patients with SSc enrolled in a large international cohort used PT/OT services in the last 3 months. Patients who used PT/OT had more severe musculoskeletal manifestations and higher pain and disability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".