Patients with Systemic Sclerosis/polymyositis Overlap Have a Worse Survival Rate Than Patients Without It
Bibliographic record
Abstract
OBJECTIVE: Studies on mortality associated with patients with systemic sclerosis (SSc) and myopathy have been limited by heterogeneous definitions of muscle involvement. The objective of this study is to determine whether homogeneous-defined SSc/polymyositis overlap (SSc-PM overlap) is associated with a worse survival rate compared with SSc without PM. METHODS: Data from the Nijmegen Systemic Sclerosis cohort were used. Incidence rates were calculated from the observed number of deaths and followup time. Survival analysis using Cox proportional hazard modeling was performed to compare survival among patients with SSc and patients with SSc-PM overlap, including controlling for confounders. All patients with SSc-PM fulfilled the Bohan and Peter criteria for PM. RESULTS: There were 24 patients with SSc-PM (5.7%) and 396 patients with SSc (94.2%). The 5- and 10-year cumulative survival rates from diagnosis were 82% and 68% for the SSc-PM group and 93% and 87% for the SSc group, respectively. Multivariate survival analysis revealed an adjusted HR of 2.34 (95% CI 1.09-5.02) for SSc-PM compared with SSc, with age at diagnosis, modified Rodnan skin score, diffuse cutaneous subtype, and male sex included as confounders. The most common cause of death among patients with SSc-PM overlap was cardiopulmonary involvement (63%), which was similar to the patients with SSc (51%). CONCLUSION: Patients with SSc-PM overlap have a worse survival rate compared with patients with SSc.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".