Variation in Access to Kidney Transplantation Across Renal Programs in Ontario, Canada
Bibliographic record
Abstract
In the United States, kidney transplant rates vary significantly across end-stage renal disease (ESRD) networks. We conducted a population-based cohort study to determine whether there was variability in kidney transplant rates across renal programs in a health care system distinct from the United States. We included incident chronic dialysis patients in Ontario, Canada, from 2003 to 2013 and determined the 1-, 5-, and 10-year cumulative incidence of kidney transplantation in 27 regional renal programs (similar to U.S. ESRD networks). We also assessed the cumulative incidence of kidney transplant for "healthy" dialysis patients (aged 18-50 years without diabetes, coronary disease, or malignancy). We calculated standardized transplant ratios (STRs) using a Cox proportional hazards model, adjusting for patient characteristics (maximum possible follow-up of 11 years). Among 23 022 chronic dialysis patients, the 10-year cumulative incidence of kidney transplantation ranged from 7.4% (95% confidence interval [CI] 4.8-10.7%) to 31.4% (95% CI 16.5-47.5%) across renal programs. Similar variability was observed in our healthy cohort. STRs ranged from 0.3 (95% CI 0.2-0.5) to 1.5 (95% CI 1.4-1.7) across renal programs. There was significant variation in kidney transplant rates across Ontario renal programs despite patients having access to the same publicly funded health care system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".