A six‐fold gradient in the incidence of type 1 diabetes at the eastern border of Finland
Bibliographic record
Abstract
OBJECTIVE: Type 1 diabetes results from gene-environment interactions in subjects with genetic susceptibility to the disease. We assessed the contribution of environmental and genetic factors to type 1 diabetes by comparing the incidence in two neighboring populations living in conspicuously different socioeconomic circumstances. RESEARCH DESIGN AND METHODS: We compared the incidence over a 10-year period (1990-99) in children younger than 15 years of age living in the Karelian Republic of Russia and in Finland. The frequency of susceptible and protective human leukocyte antigen (HLA)-DQ alleles was analyzed in 400 non-diabetic schoolchildren from Russian Karelia and 1000 Finnish subjects. RESULTS: The average annual age-adjusted incidence of type 1 diabetes was lower in Russian Karelia than in Finland: 7.4 per 100000 (95% confidence interval 3.5-11.3) versus 41.4 per 100000 (37.3-45.5), while there were no differences in the frequency of the HLA DQ genotypes predisposing to type 1 diabetes in the background populations. The incidence rate did not differ significantly between different ethnic groups in Russian Karelia (Finns/Karelians, Russians, others). CONCLUSIONS: There is a close to six-fold gradient in the incidence of type 1 diabetes between Russian Karelia and Finland, although the predisposing HLA DQ genotypes are equally frequent in the two populations. This suggests that environmental factors contribute to this steep difference in the incidence rate between these adjacent regions.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".