Variation in Kidney Transplant Referral Across Chronic Kidney Disease Programs in Ontario, Canada
Bibliographic record
Abstract
Background:Eligible patients with kidney failure should have equal access to kidney transplantation. Transplant referral is the first crucial step toward receiving a kidney transplant; however, studies suggest substantial variation in the rate of kidney transplant referral across regions. The province of Ontario, Canada, has a public, single-payer health care system with 27 regional chronic kidney disease (CKD) programs. The probability of being referred for kidney transplant may not be equal across CKD programs.Objective:To determine whether there is variability in kidney transplant referral rates across Ontario’s CKD programs.Design:Population-based cohort study using linked administrative health care databases from January 1, 2013, to November 1, 2016.Setting:Twenty-seven regional CKD programs in the province of Ontario, Canada.Patients:Patients approaching the need for dialysis (advanced CKD) and patients receiving maintenance dialysis (maximum follow-up: November 1, 2017).Measurements:Kidney transplant referral.Methods:We calculated the 1-year unadjusted cumulative probability of kidney transplant referral for Ontario’s 27 CKD programs using the complement of Kaplan-Meier estimator. We calculated standardized referral ratios (SRRs) for each CKD program, using expected referrals from a 2-staged Cox proportional hazards model, adjusting for patient characteristics in the first stage. Standardized referral ratios with a value less than 1 were below the provincial average (maximum possible follow-up of 4 years 10 months). In an additional analysis, we grouped CKD programs according to 5 geographic regions.Results:Among 8641 patients with advanced CKD, the 1-year cumulative probability of kidney transplant referral ranged from 0.9% (95% confidence interval [CI]: 0.2%-3.7%) to 21.0% (95% CI: 17.5%-25.2%) across the 27 CKD programs. The adjusted SRR ranged from 0.2 (95% CI: 0.1-0.4) to 4.2 (95% CI: 2.1-7.5). Among 6852 patients receiving maintenance dialysis, the 1-year cumulative probability of transplant referral ranged from 6.4% (95% CI: 4.0%-10.2%) to 34.5% (95% CI: 29.5%-40.1%) across CKD programs. The adjusted SRR ranged from 0.2 (95% CI: 0.1-0.3) to 1.8 (95% CI: 1.6-2.1). When we grouped CKD programs according to geographic region, we found that patients residing in Northern regions had a substantially lower 1-year cumulative probability of transplant referral.Limitations:Our cumulative probability estimates only captured referrals within the first year of advanced CKD or maintenance dialysis initiation.Conclusions:There is marked variability in the probability of kidney transplant referral across CKD programs operating in a publicly funded health care system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".