Three-year trajectories of disability and fatigue in systemic sclerosis: a cohort study.
Bibliographic record
Abstract
OBJECTIVES: Functional disability and fatigue are important consequences of systemic sclerosis (SSc), but little is known about their course over time. The aim of this study was to identify and characterise homogeneous subgroups with distinct 3-year trajectories of disability and fatigue, separately. METHODS: A 3-year cohort study including 215 patients with SSc was conducted. Functional disability was assessed using the Health Assessment Questionnaire-Disability Index (HAQ-DI). Fatigue was assessed using the SF-36 Vitality subscale. Longitudinal trajectories were identified using latent class growth analyses (LCGA). Baseline patient characteristics were compared across classes using multivariable logistic regression. RESULTS: Two disability classes were identified: a 'low' group (n=133) with low baseline HAQ-DI scores (intercept=0.48) and slight, statistically non-significant deterioration over time (slope=0.01), and a 'high' group (n=82) with high baseline HAQ-DI scores (intercept=1.63) and also slight, statistically non-significant deterioration over time (slope=0.01). Patients in the high disability group were more likely to be female, have higher fatigue, more helplessness, and less emotion-focused coping. Two fatigue classes were identified: an 'average' group (n=99) with average baseline Vitality scores (intercept=53.9) and slight, statistically non-significant deterioration over time (slope=-0.23), and a 'high' fatigue group (n=116) with low baseline Vitality scores (intercept=39.8) and also slight, but non-significant deterioration over time (slope=-0.15). Patients in the high fatigue group were more likely to be female, report more impact of lung involvement, and less acceptance. CONCLUSIONS: Functional disability and fatigue trajectories in SSc were relatively stable over a 3-year period, and differences in baseline scores, but not slopes, defined classes.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".