Is the Kidney Donor Risk Index a Useful Tool in Non-US Patients?
Bibliographic record
Abstract
Background: Deceased donor kidney allocation in the United States is guided by the Kidney Donor Risk Index (KDRI). The generalizability of the KDRI beyond the United States has not been widely studied. Objective: To assess the generalizability of the KDRI in a cohort of non-US (Canadian) deceased donor kidney transplant recipients. Design: Population-based retrospective cohort study. Setting: Ontario, Canada. Patients: Recipients of deceased donor kidneys from January 1, 2005, to March 31, 2011. Methods: Using administrative data, we analyzed a cohort of deceased donor kidney recipients in Ontario, Canada. The Kaplan-Meier method and Cox proportional hazards models were used to assess the relationship between KDRI and the outcomes of graft loss and death. KDRI was modeled continuously and categorically. The ability of models with KDRI to predict recipient outcomes beyond donor age was also explored. Model discrimination was assessed using c-statistics, evaluated at 5 years of follow-up. Results: A total of 1299 consecutive deceased donor kidney transplant recipients were included. The median follow-up was 5.5 years. Mean donor age increased from 27 to 64 years across ascending KDRI quintiles. The adjusted relative hazards (95% confidence interval) for total graft loss from Q2 to Q5 (referent = Q1) were 1.27 (0.89-1.80), 1.58 (1.13-2.22), 1.43 (1.01-2.02), and 2.15 (1.54-2.99), respectively. Increased relative hazards across KDRI quintiles were also observed for death-censored graft loss, but not death with graft function. All-cause mortality was increased for the highest KDRI quintile only. In this cohort, a model with KDRI performed better than a model with donor age alone ( P = .009). Limitations: Large health care databases may have precluded the complete capture of covariate data. Conclusions: In conclusion, the KDRI is generalizable to Canadian patients in Ontario and may help inform risk assessment beyond donor age. The performance of KDRI in other non-US settings, and the need for additional model refinement, warrants 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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".