The Canadian Experience Using the Expanded Criteria Donor Classification for Allocating Deceased Donor Kidneys for Transplantation
Bibliographic record
Abstract
BACKGROUND: Although the outcomes of transplantation with expanded criteria donor (ECD) kidneys are inferior to non-ECD transplants in the USA, the impact of the ECD classification on Canadian kidney transplant recipients is not known. OBJECTIVES: The objective of the study was to assess the performance of the US-derived ECD classification among deceased donor kidney transplant recipients in a Canadian setting. DESIGN: This study was a population-based cohort study. SETTING: The study was conducted in all adult kidney transplant centers in the province of Ontario. PATIENTS: The patients were incident-deceased donor kidney transplant recipients from January 1, 2005 to March 31, 2011. MEASUREMENTS: Study subjects were identified through the Trillium Gift of Life Network and linked to healthcare databases in Ontario. ECD status was based on age, hypertension, kidney function, and stroke-related death. Outcomes of interest included graft loss, death, and delayed graft function. METHODS: The Kaplan-Meier product limit method was used to graphically assess time to graft loss or death. Multivariable Cox proportional hazards models were used to assess graft loss or death as a function of ECD status. Multivariable logistic regression models were fitted for the outcome of delayed graft function. RESULTS: Of 1422 deceased donor kidney transplants, 325 (23 %) were from ECDs. The median donor age was 63 vs. 42 years for ECD vs. non-ECD, respectively. The 5-year cumulative incidence of total graft loss was 29.2 % in ECD and 20.7 % in non-ECD kidney transplants. The relative hazards for total graft loss (HR 1.48 [95 % CI, 1.10; 2.00]) and death-censored graft loss (HR 1.80 [95 % CI, 1.19, 2.71]) were increased in ECD vs. non-ECD transplants. Increased relative risks were also observed for death and delayed graft function, albeit not statistically significant. LIMITATIONS: Although comprehensive in coverage and outcome ascertainment, the available details on covariate data may be limited in large healthcare databases. CONCLUSIONS: The ECD classification identifies kidneys at increased risk for graft loss in Canadian patients. The performance of more granular measures of donor risk (e.g., Kidney Donor Risk Index) and its impact on organ allocation/utilization in Canadian patients requires further study.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".