Clinical utility of HNF1A genotyping for diabetes in aboriginal Canadians.
Bibliographic record
Abstract
OBJECTIVE: To determine the diagnostic performance characteristics of HNF1A genotyping for diabetes and impaired glucose tolerance (IGT) in Canadian Oji-Cree Indians. RESEARCH DESIGN AND METHODS: We studied all Oji-Cree subjects > or = 50 years of age (96 subjects) who had participated in a community-wide prevalence survey for type 2 diabetes. Subjects were classified either as having "disease," which included type 2 diabetes and IGT, or not. All subjects were genotyped for the HNF1A G319S mutation. RESULTS: The prevalence of disease in this group was 65.7%, of whom 71.4% had type 2 diabetes. For a carrier of HNF1A S319, the specificity, sensitivity, and positive and negative predictive values were 97.0, 30.1, 95.0, and 42.1%, respectively. When the pretest disease prevalence was accounted for, the probability of disease after a positive test was 97.2%, and the probability of disease after a negative test was 42.2%. The values were very similar for the subgroup of subjects with type 2 diabetes alone. CONCLUSIONS: The HNF1A genotype appears to be the most specific genetic test yet reported for the prediction of a common multifactorial disease by applying present-day standards of clinical epidemiology in molecular genetics. A positive test result had particular diagnostic value in the Oji-Cree: a subject with HNF1A S319 was virtually certain of having diabetes or IGT by 50 years of age. In contrast, a subject without HNF1A S319 had a reduced risk compared with the age-specific prevalence but was not totally risk-free. Because HNF1A S319 was not the only predisposing factor for diabetes in the Oji-Cree, subjects without HNF1A S319 were still at some risk for diabetes or IGT.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".