Divergent trends in the incidence of end‐stage renal disease due to Type 1 and Type 2 diabetes in Europe, Canada and Australia during 1998–2002
Bibliographic record
Abstract
AIMS: To describe the variation in geographical distribution of end-stage renal disease (ESRD) due to Type 1 and Type 2 diabetes, and to calculate recent trends in incidence in predominantly white populations. METHODS: Estimation of age- and sex-standardized incidence of ESRD by type of diabetes, and temporal trends, in population-based data for persons aged 30-44, 45-54 or 55-64 years newly treated for ESRD during 1998-2002 in eight countries or regions of Europe, and Non-Indigenous Canadians and Australians. RESULTS: The incidence of ESRD due to Type 1 diabetes at age 30-44 years correlated with published rates of childhood-onset insulin dependent diabetes mellitus (P = 0.0025). ESRD due to Type 2 diabetes was uncommon before 45 years of age; in older persons, the highest rates (in Canada and Austria) were five times the lowest rates (in Norway and the Basque region). Rates of ESRD due to Type 1 diabetes fell, per year, by 6.4%[95% confidence interval (CI): 2.1-10.6%) in persons aged 30-44 years, and by 7.7% (95% CI: 2.4-12.7%] in those aged 45-54 years. In contrast, rates of ESRD due to Type 2 diabetes increased annually by 16% (95% CI: 5-28%) in the 30-44-year age group, 11% (95% CI: 6-16%) at 45-54 years, and 9% (95% CI: 5-14%) at 55-64 years. CONCLUSIONS: Modern prevention has reduced progression of nephropathy to ESRD due to Type 1 diabetes, but the continuing rise of ESRD due to Type 2 diabetes represents a failure of current disease control measures that has serious public health implications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".