Defining pre-emptive living kidney donor transplantation as a quality indicator
Bibliographic record
Abstract
Quality indicators in kidney transplants are needed to identify care gaps and improve access to transplants. We used linked administrative health care databases to examine multiple ways of defining pre-emptive living donor kidney transplants, including different patient cohorts and censoring definitions. We included adults from Ontario, Canada with advanced chronic kidney disease between January 1, 2013, to December 31, 2018. We created 4 unique incident patient cohorts, varying the eligibility by the risk of progression to kidney failure and whether individuals had a recorded contraindication to kidney transplant (eg, home oxygen use). We explored the effect of 4 censoring event definitions. Across the 4 cohorts, size varied substantially from 20 663 to 9598 patients, with the largest reduction (a 43% reduction) occurring when we excluded patients with ≥1 recorded contraindication to kidney transplantation. The incidence rate (per 100 person-years) of pre-emptive living donor kidney transplant varied across cohorts from 1.02 (95% CI: 0.91-1.14) for our most inclusive cohort to 2.21 (95% CI: 1.96-2.49) for the most restrictive cohort. Our methods can serve as a framework for developing other quality indicators in kidney transplantation and monitoring and improving access to pre-emptive living donor kidney transplants in health care systems.
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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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".