Investigating the trajectory of functional disability in systemic sclerosis: group-based trajectory modelling of the Health Assessment Questionnaire-Disability Index
Bibliographic record
Abstract
OBJECTIVES: To identify the trajectories and clinical associations of functional disability in systemic sclerosis (SSc). METHODS: Australian Scleroderma Cohort Study (ASCS) participants meeting ACR/EULAR criteria for SSc recruited within 5 years of disease onset, with ≥2 Health Assessment Questionnaire-Disability Index (HAQ-DI) scores were included. Group based trajectory modelling (GBTM) was used to identify the number and shape of HAQ-DI trajectories. Between group comparisons were made using the chi-squared test, two-sample t-test or Wilcoxon rank-sum test as appropriate. Multiple logistic regression was used to identify features associated with trajectory group membership. Survival analyses were performed using Kaplan Meier and Cox proportional hazard modelling. RESULTS: We identified two HAQ-DI trajectory groups within 426 ASCS participants with incident SSc: low-stable disability (n=221, 52%), and high-increasing disability (n=205, 48%). Participants with high-increasing disability were older at disease onset, more likely to have diffuse SSc (dcSSc), cardiopulmonary disease, multimorbidity, digital ulcers, and gastrointestinal involvement (all p≤0.01), as was use of immunosuppression (p<0.01). Multimorbidity was associated with high-increasing trajectory group membership (OR3.1, 95%CI1.1-8.8, p=0.04); independently, multiple SSc features were also strongly associated including dcSSc (OR2.3, 95%CI1.3-4.2, p<0.01), proximal weakness (OR7.3, 95%CI2.0-27.1, p<0.01) and joint contractures (OR2.7, 95%CI1.3-5.3, p<0.01). High-increasing physical disability was associated with an almost two-fold increased risk of mortality (HR1.9, 95%CI1.0-3.8, p=0.05), and higher symptom burden. CONCLUSIONS: Two trajectories of functional disability in SSc were identified. Those with high-increasing functional disability had a distinct clinical phenotype and worse survival compared to those with low-stable functional disability. These data highlight the pervasive nature of physical disability in SSc, and its prognostic importance.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".