P.447: Impact of donor smoking history on kidney transplant recipient outcomes: A systematic review and meta-analysis.
Bibliographic record
Abstract
Background: Impact of donor smoking history on kidney transplant recipient outcomes is undefined. Methods: We systematically searched, critically appraised, and summarized associations between donor smoking and primary outcomes of death-censored and all-cause graft failure (DCGF, ACGF), and secondary outcomes of allograft histology, delayed graft function, serum creatinine, estimated glomerular filtration rate, and mortality. We searched MEDLINE, Embase, and Cochrane Databases from 2000-2023. Risk of bias was assessed using Risk of Bias in Non-randomized Studies – of Exposure tool. Quality of evidence was assessed by Grading of Recommendations Assessment, Development and Evaluation Working Group recommendations. We pooled results using inverse variance, random-effects model and reported hazard ratios for time-to-event outcomes or binomial proportions. Statistical heterogeneity was assessed with I2 statistic. Results: From 1785 citations, we included 17 studies. Donor smoking was associated with modestly increased DCGF (HR 1.05 (95% CI: 1.01, 1.09); I2 = 0%; low quality of evidence), predominantly in deceased donors, and ACGF in adjusted analyses (HR 1.12 (95% CI: 1.06, 1.19); I2 = 20%; very low quality of evidence). Other outcomes could not be pooled meaningfully. Conclusions: Kidney donor smoking history was associated with modestly increased risk of death-censored graft failure and all-cause graft failure. This review emphasizes the need for further research, standardized reporting, and thoughtful consideration of donor factors like smoking in clinical decision-making on kidney utilization and allocation. CR is supported by a Kidney Research Scientist Core Education and National Training Program (KRESCENT) salary award co-funded by the Kidney Foundation of Canada, the Canadian Society of Nephrology, and the Canadian Institutes of Health Research (CIHR). The funders had no role in defining the content of this article.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.027 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".