Systemic Sclerosis in Canada’s North American Native Population: Assessment of Clinical and Serological Manifestations
Bibliographic record
Abstract
OBJECTIVE: Certain North American Native (NAN) populations are known to have higher rates of systemic sclerosis (SSc) compared to non-NAN; however, little is known of the specific disease characteristics in this population in Canada. This study compares the clinical and serological manifestations of SSc in NAN and white patients. METHODS: This cross-sectional, multicenter study included subjects enrolled in the Canadian Scleroderma Research Group registry between September 2004 and June 2012. Subjects were evaluated with complete medical histories, physical examinations, and self-questionnaires. Ethnicity was defined by self-report. Disease characteristics were compared between NAN and white patients and multivariate analyses were performed to determine the independent association between ethnicity and various clinical manifestations. RESULTS: Of 1278 patients, 1038 (81%) were white, 71 (6%) were NAN, and 169 (13%) were classified as non-white/non-NAN. There were important differences between NAN and white subjects with SSc. In multivariate analysis adjusting for socioeconomic differences and smoking status, NAN ethnicity was an independent risk factor for the severity of Raynaud phenomenon and more gastrointestinal symptoms, and was associated with a nonsignificant increase in the presence of digital ulcers. CONCLUSION: NAN patients with SSc have a distinct clinical phenotype. Our study provides a strong rationale to pursue further research into genetic and environmental determinants of 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".