Ethnic Variations in Systemic Sclerosis Disease Manifestations, Internal Organ Involvement, and Mortality
Bibliographic record
Abstract
OBJECTIVE: A multiethnic systemic sclerosis (SSc) cohort study to evaluate ethnic variations in disease manifestations, internal organ involvement, and survival. METHODS: Adults who fulfilled the American College of Rheumatology/European League Against Rheumatism classification criteria for SSc between 1970 and 2017 were included. Self-reported ethnicity was categorized as European-descent white, Afro-Caribbean, Hispanic, Arab, East Asian, South Asian, First Nations, or Persian. The primary outcome was the time from diagnosis to death from all causes. Survival probabilities and median survival times were determined using Kaplan-Meier survival curves. RESULTS: There were 1005 subjects evaluated, the majority of whom were European-descent white (n = 745, 74%), Afro-Caribbean (n = 58, 6%), South Asian (n = 70, 7%), and East Asian (n = 80, 8%). Compared to European-descent white subjects, East Asians less frequently had calcinosis (29% vs 9%, p = 0.002) and esophageal dysmotility (88% vs 69%, p = 0.002); Afro-Caribbeans more frequently had interstitial lung disease (31% vs 53%, p = 0.007); and First Nations subjects more frequently had diffuse cutaneous disease (35% vs 56%, p = 0.02) and diabetes (5% vs 33%, p = 0.03). We found no difference in the short-term survival across ethnicities. Hispanic subjects have better longterm survival (81.3%, 95% CI 63-100) compared to European-descent white subjects (55%, 95% CI 51-60). East Asians appear to have the longest median survival time (43.3 yrs) and Arabs the shortest median survival time (15 yrs). There was no significant difference in median survival times between Afro-Caribbean and European-descent white subjects (22.2 vs 22.6 yrs). CONCLUSION: Ethnic variations in some SSc disease manifestations are observed. However, this does not result in significant differences in short-term survival but may affect longterm survival.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".