Survival and Causes of Death in an Unselected and Complete Cohort of Norwegian Patients with Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To determine survival and causes of death in an unselected and complete cohort of Norwegian patients with systemic sclerosis (SSc) compared to the background population. METHODS: Multiple methods were used to identify every patient with SSc living in southeast Norway, with a denominator population of 2,707,012, between 1999 and 2009. All patients who met either the American College of Rheumatology criteria or the Medsger and LeRoy criteria for SSc were included. Every patient was matched for sex and age with 15 healthy controls drawn from the national population registry. Vital status at January 1, 2010, was provided for patients and controls by the national population registry. Causes of death were obtained from death certificates and by chart review. RESULTS: Forty-three (14%) of 312 patients with SSc died during the study period. The standardized mortality rate (SMR) was estimated to be 2.03 for the entire cohort and 5.33 for the subgroup with diffuse cutaneous (dc) SSc. The 5- and 10-year survival rates were 91% and 70%, respectively, for dcSSc and 98% and 93% for limited cutaneous (lc) SSc. Causes of death were related to SSc in 24/43 (56%) patients, mostly cardiopulmonary diseases (n = 13), including pulmonary hypertension (n = 8). Factors associated with fatal outcome included male sex, dcSSc, pulmonary hypertension, and interstitial lung disease. CONCLUSION: Compared to the Norwegian background population, our cohort of 312 unselected patients with SSc had decreased survival. The survival rates observed were, however, better than those previously reported from SSc referral centers.
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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 | 0.001 | 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) | 0.001 | 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".