Risk Factors for 1-Year Graft Loss After Kidney Transplantation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: With expansion of the pool of kidney grafts, through the use of higher-risk donors, and increased attention to donor management strategies, the 1-year graft survival rate is subject to change. It is, therefore, useful to elucidate 1-year graft survival rates by dissecting the characteristics of the low-risk and high-risk kidney transplant cases. The objective of our study was to evaluate factors purported to influence the risk of 1-year graft loss in kidney transplant recipients. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We searched bibliographic databases from 2000 to 2017 and included observational studies that measured the association between donor, recipient, the transplant operation, or early postoperative complications, and 1-year death-censored graft loss. RESULTS: We identified 35 eligible primary studies, with 20 risk factors amenable to meta-analysis. Six factors were associated with graft loss, with moderate to high degree of certainty: donor age (hazard ratio [HR], 1.11 per 10-year increase; 95% confidence interval [95% CI], 1.04 to 1.18), extended criteria donors (HR, 1.35; 95% CI, 1.28 to 1.42), deceased donors (HR, 1.54; 95% CI, 1.32 to 1.82), number of HLA mismatches (HR, 1.08 per one mismatch increase; 95% CI, 1.07 to 1.09), recipient age (HR, 1.17 per 10-year increase; 95% CI, 1.09 to 1.25), and delayed graft function (HR, 1.89; 95% CI, 1.46 to 2.47) as risk factors for 1-year graft loss. Pooled analyses also excluded, with a high degree of certainty, any associations of cold ischemia time, recipient race, pretransplant body mass index, diabetes, and hypertension with 1-year graft loss. CONCLUSIONS: Recipient age, donor age, standard versus extended criteria donor, living versus deceased donor, HLA mismatch, and delayed graft function all predicted 1-year graft survival. The effect of each risk factor is small.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.015 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".