Defining Referral for a Kidney Transplant Evaluation as a Quality Indicator: A Population-Based Cohort Study
Bibliographic record
Abstract
Background: Quality indicators are required to identify gaps in care and to improve equitable access to kidney transplants. Referral to a transplant center for an evaluation is the first step toward receiving a kidney transplant, yet widespread reporting on this metric is lacking. Objective: The objective was to use administrative health care databases to examine multiple ways to define referral for a kidney transplant evaluation by varying clinical inclusion criteria, definitions for end of follow-up, and statistical methodologies. Design: This is a population-based cohort study. Setting: This study linked administrative health care databases in Ontario, Canada. Patients: Adults from Ontario, Canada, with advanced chronic kidney disease (CKD) between April 1, 2017, and March 31, 2018. Measurements: The primary outcome was the 1-year cumulative incidence of kidney transplant referral. Methods: We created several patient cohort definitions, varying patient transplant eligibility by health status (eg, whether patients had a recorded contraindication to transplant). We presented results by advanced CKD status (ie, patients approaching the need for dialysis vs receiving maintenance dialysis) and by method of cohort entry (ie, incident only vs prevalent and incident patients combined), resulting in 12 unique cohorts. Results: Sample size varied substantially from 414 to 4128 depending on the patient cohort definition, with the largest reduction in cohort size occurring when we restricted to a "healthy" (eg, no evidence of cardiovascular disease) group of patients. The 1-year cumulative incidence of transplant referral varied widely across cohorts. For example, in the incident maintenance dialysis population, the cumulative incidence varied more than 2-fold from 16.3% (95% confidence interval [CI] = 15.0%-17.7%) using our most inclusive cohort definition to 40.0% (95% CI = 36.0%-44.5%) using our most restrictive "healthy" cohort of patients. Limitations: Administrative data may have misclassified individuals' eligibility for kidney transplant. Conclusions: These results can be used by jurisdictions to measure transplant referral, a necessary step in kidney transplantation that is not equitable for all patients. Adoption of these indicators should drive quality improvement efforts that increase the number of patients referred for transplantation and ensure equitable access for all patient groups.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".