Disability in Systemic Sclerosis — A Longitudinal Observational Study
Bibliographic record
Abstract
OBJECTIVE: To assess disability in systemic sclerosis (SSc) longitudinally and to identify disease-specific determinants, after accounting for informative patient dropout. METHODS: We performed a multicenter, longitudinal study of 745 patients with SSc followed in the Canadian Scleroderma Research Group registry. Disability was assessed using the Health Assessment Questionnaire (HAQ). Longitudinal changes in disability were modeled using statistical approaches accounting for various levels of patient dropout. RESULTS: In all the models, disability in SSc worsened over time. The magnitude of the worsening was small when patient dropout was assumed to be completely at random (increase in the HAQ of 0.022, 95% CI 0.002-0.042, per year). After accounting for different levels of informative patient dropout, the increase in the HAQ ranged from 0.039 (95% CI 0.018-0.061) per year to 0.071 (95% CI 0.048-0.094) per year. Thus, using the most conservative of these estimates, this was equivalent to an increase in the HAQ of 0.12 over 3 years. The disease correlates found to be most closely associated with disability were diffuse disease and breathing problems. CONCLUSION: Our study provides strong evidence that SSc causes increased disability over time, with breathing problems and disease type being the strongest predictors of disability. Statistical modeling accounting for informative patient dropout is necessary to properly assess the outcomes of patients followed longitudinally.
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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 | 0.004 | 0.006 |
| 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 | 0.000 | 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".