Clinical Correlates of Self-reported Physical Health Status in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) is a multisystem disease associated with impaired health-related quality of life (HRQOL). Our objective was to identify the clinical characteristics that correlate with the physical health status of patients with SSc, as assessed by the Medical Outcomes Trust Short Form-36 (SF-36). METHODS: Cross-sectional, multicenter study of 416 patients from the Canadian Scleroderma Research Group Registry. Patients were assessed with detailed clinical histories, medical examinations, and self-administered SF-36. Multiple linear regression was used to assess the relationship between selected demographic and clinical variables and the SF-36 Physical Component Summary (PCS) score. RESULTS: The greatest impairments in the SF-36 were in the domains measuring physical health, and the mean SF-36 PCS score was 37.5 (+/-11.2). In multivariate analysis, significant clinical predictors of the SF-36 PCS were shortness of breath, number of gastrointestinal problems, skin score, swollen joint count, and age. The final model explained 47% of the variance in the SF-36 PCS. CONCLUSION: Clinical characteristics identified as significant correlates of the self-reported physical health status in SSc should each be targets of intervention in order to improve the HRQOL of patients with this disease.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".