Impact of donor-recipient size mismatch on post-transplant outcomes in kidney transplant recipients: A systematic review
Bibliographic record
Abstract
BACKGROUND: Donor-recipient size mismatch builds on the nephron dosing concept, but studies suggest unclear associations with kidney allograft outcomes. METHODS: We systematically searched, critically appraised, and summarized associations between donor-recipient size mismatch and primary outcome of death-censored graft failure, secondary outcomes of kidney function, all-cause graft failure, and mortality. The study protocol was registered a priori on PROSPERO (ID CRD42023455394). We searched MEDLINE, Embase, and Cochrane Databases from 1946 to 2025 for studies evaluating adult kidney transplant recipients. We excluded non-English or unavailable full texts, and studies with donors <16 years old. Risk of bias was assessed using Risk of Bias in Non-randomized Studies - of Exposure tool. Studies were narratively synthesized; marked heterogeneity precluded quantitative meta-analysis. RESULTS: From 1521 citations, 56 studies were included. Sample sizes ranged from 23 to 238,895 donor-recipient pairs (median 214, IQR [95, 807]), with follow-up from 1 week to >20 years. Studies varied in size mismatch definitions, exposure subgrouping, outcomes, patient populations, and follow-up period. Overall, 32 % demonstrated worse kidney allograft outcomes with unfavorable size mismatch, 9 % showed no association, and 59 % reported mixed findings. All studies had high or very high risk of bias. CONCLUSIONS: Available studies do not provide strong evidence to support or reject the idea of nephron underdosing, however existing reports were generally of poor quality, with high or very high risk of bias. Due to data heterogeneity, quantitative meta-analysis was not performed. Well-designed studies with clear exposure definitions, standardized outcome assessments, appropriate confounder control, adequate follow-up, and robust statistical analyses remain a priority.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".