Islet autoimmunity in young First Nations women with prediabetes and type 2 diabetes
Bibliographic record
Abstract
AIMS: Type 1 diabetes in First Nations peoples is low yet type 2 diabetes is at epidemic proportions. This study aimed to determine the prevalence of islet autoimmunity in First Nations women with dysglycaemia and its association with clinical features. METHODS: One hundred and eighty First Nations women with prediabetes (n = 51) or type 2 diabetes (n = 129) were screened for any of GAD, IA-2 and ZnT8 autoantibodies using 3Screen ELISA, then ELISA for individual autoantibodies for positive screens. Associations between individual antibody positivity and clinical and metabolic characteristics were assessed. RESULTS: Of the 180 women, 16% were positive on 3Screen, comprising 10/51 with prediabetes and 18/129 with diabetes. Sixteen of 28 positive on 3Screen were also positive for at least one individual autoantibody on ELISA testing; with 5/51 (10%) with prediabetes and 11/129 (9%) with diabetes. Individual autoantibody positivity was not associated with clinical and metabolic characteristics or markers of inflammation. CONCLUSIONS: The proportion of individual autoantibody positivity among younger First Nations women with prediabetes or type 2 diabetes was 9%. Islet autoantibody positivity was not associated with a distinct clinical phenotype in this group. Longitudinal follow-up will allow assessment of glycaemic trajectories and clinical outcomes in younger First Nations women.
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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 | 0.000 | 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) | 0.002 | 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".