The Value of Studying Clinical and Serologic Phenotypes in North American Native Populations with Autoimmune Disease
Bibliographic record
Abstract
In the last 10–15 years there has been increasing awareness of a high rheumatic disease burden in North American Natives (NAN), resulting in a growing number of studies describing clinical and serological phenotypes in these populations. But what is the value of these largely descriptive studies? In this issue of The Journal , Bacher and colleagues add to the body of literature on rheumatic disease in NAN populations, describing the manifestations and symptoms of scleroderma (systemic sclerosis; SSc) in a group of 71 Native Canadians1. This is the largest NAN cohort ever described with this relatively rare disease, and the authors suggest possible differences in the phenotype of SSc compared to white populations. Variability in the phenotypic expression of many autoimmune diseases between different ethnicities has long been recognized; in NAN populations autoimmune disease is generally recognized to be severe2,3,4. At the very least, descriptions of disparate burdens of disease can help guide public health policy, and direct increased health resources to affected ethnic groups. Ethnicity, however, is a complex concept, which includes racial designations or genotypic groupings, but transcends them, representing instead the aggregate of cultural practices, lifestyle patterns, social influences, religious pursuits, and racial characteristics that shape the distinctive identity of a community5. Autoimmune diseases, such as SSc and systemic lupus erythematosus (SLE), are known to arise from a complex interaction between genetic, environmental, socioeconomic, cultural, and … Address correspondence to Dr. Peschken; E-mail: cpeschken{at}exchange.hsc.mb.ca
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".