The growing volume of diabetes-related dialysis: a population based study
Bibliographic record
Abstract
BACKGROUND: End-stage renal failure requiring dialysis is one of the most serious complications of diabetes mellitus, and diabetes is the most common cause of end-stage renal failure. The aim of this large, observational study is to describe the population-based incidence and prevalence rates and outcomes of diabetic individuals in Ontario, Canada who require dialysis therapy. METHODS: Two cohorts of patients, those with diabetes and those without, were created between April 1, 1994 and March 31, 2000 (total of approximately 8.4 million) and followed until March 31, 2001 using several large, linked administrative databases at the Institute for Clinical Evaluative Sciences. The incidence, prevalence and mortality on dialysis for each cohort were determined. A multivariate Cox proportional hazards analysis, adjusting for age, sex and co-morbidity, was used to determine the independent impact of diabetes on patient survival. RESULTS: The average annual incidence rate of dialysis was 12 times greater in persons with diabetes (130 per 100,000) vs without diabetes (11 per 100,000). By 1999-2000, diabetic patients comprised 51% of the incident dialysis population. The average annual prevalence rate was 10 times greater in the diabetic cohort. Patients with diabetes had more co-morbidities at the start of dialysis and poorer 3 year survival (55 vs 68%; P < 0.0001). CONCLUSIONS: The incident and prevalent rates of dialysis for patients with diabetes mellitus are many times the rates of those without diabetes. Patients with diabetes mellitus often start dialysis with significant co-morbidities, which may contribute to the relatively high rate of mortality on dialysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".