Damage Trajectories in Systemic Sclerosis Using <scp>Group‐Based</scp> Trajectory Modeling
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) is an autoimmune disease characterized by progressive organ damage, which can be measured using the Scleroderma Clinical Trials Consortium Damage Index (SCTC-DI). We aimed to identify whether distinct trajectories of damage accrual exist and to determine which variables are associated with different trajectory groups. METHODS: Incident cases of SSc (<2 years) were identified in the Australian Scleroderma Interest Group and Canadian Scleroderma Research Group prospective registries. Group-based trajectory modeling was used to identify SCTC-DI trajectories over the cohort's first 5 annual visits. Baseline variables associated with trajectory membership in a univariate analysis were examined in multivariable models. RESULTS: A total of 410 patients were included. Three trajectory groups were identified: low (54.6%), medium (36.2%), and high (10.3%) damage. Patients with faster damage accrual had higher baseline SCTC-DI scores. Older age (odds ratio [OR] 1.57 [95% confidence interval (95% CI) 1.18-2.10]), male sex (OR 2.55 [95% CI 1.10-5.88]), diffuse disease (OR 6.7 [95% CI 2.57-17.48]), tendon friction rubs (OR 5.4 [95% CI 1.86-15.66]), and elevated C-reactive protein level (OR 1.98 [95% CI 1.49-2.63]) increased the odds of being in the high-damage group versus the reference (low damage), whereas White ethnicity (OR 0.31 [95% CI 0.12-0.75]) and anticentromere antibodies (OR 0.24 [95% CI 0.07-0.77]) decreased the odds. CONCLUSION: We identified 3 trajectories of damage accrual in a combined incident SSc cohort. Several characteristics increased the odds of belonging to worse trajectories. These findings may be helpful in recognizing patients in whom early aggressive treatment is necessary.
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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 | 0.003 | 0.006 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".