A Propensity Score–weighted Comparison of Outcomes Between Living and Standard Criteria Deceased Donor Kidney Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: Consider a theoretical situation in which 2 patients with similar baseline characteristics receive a kidney transplant on the same day: 1 from a standard criteria deceased donor, the other from a living donor. Which kidney transplant will last longer? METHODS: We conducted a population-based cohort study using linked administrative healthcare databases from Ontario, Canada, from January 1, 2005, to March 31, 2014, to evaluate several posttransplant outcomes in individuals who received a kidney transplant from a standard criteria deceased donor (n = 1523) or from a living donor (n = 1373). We used PS weighting using overlap weights, a novel weighting method that emphasizes the population of recipients with the most overlap in baseline characteristics. RESULTS: Compared with recipients of a living donor, the rate of all-cause graft failure was not statistically higher for recipients of a standard criteria deceased donor (hazard ratio, 1.1; 95% confidence interval [CI], 0.8-1.6). Recipients of a standard criteria deceased donor, compared with recipients of a living donor had a higher rate of delayed graft function (23.6% versus 18.7%; odds ratio, 1.3; 95% CI, 1.0-1.6) and a longer length of stay for the kidney transplant surgery (mean difference, 1.7 d; 95% CI, 0.5-3.0). CONCLUSIONS: After accounting for many important donor and recipient factors, we failed to observe a large difference in the risk of all-cause graft failure for recipients of a standard criteria deceased versus living donor. Some estimates were imprecise, which meant we could not rule out the presence of smaller clinically important effects.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".