Frailty and post-transplant adverse outcomes among kidney transplant recipients
Bibliographic record
Abstract
INTRODUCTION: Frailty is a good predictor of adverse outcomes among older patients, especially those who have undergone surgery. The prevalence of frailty among kidney transplant candidates is higher than the general population. This study aimed to explore the predictive value of frailty on post-transplant adverse outcomes among kidney recipients. METHODS: A systematic review was performed for relevant studies until May 20, 2022, using four databases (Embase, Medline, Cochrane, and PsycINFO) for prospective design studies (PROSPERP: CRD42022331022). Random-effect meta-analysis modeling was undertaken in RevMan 5.3 to estimate the predictive value of frailty on adverse outcomes after kidney transplant. RESULTS: This systematic review included 14 studies, eight of which were suitable for meta-analysis. Frailty increased the risk of mortality (pooled hazard ratio [HR] 1.98, 95% confidence interval [CI] 1.48-2.64), surgical complications (risk ratio [RR] 2.14, 95% CI 1.01-4.54), death-censored graft failure (DCGF) (pooled HR 3.31, 95% CI 1.27-8.62), length of stay (LOS) (pooled RR 1.59, 95% CI 1.05-2.39), length of stay ≥2 weeks (pooled odds ratio [OR] 1.72, 95% CI 1.26-2.35), and other common adverse outcomes among kidney transplant recipients. CONCLUSIONS: Frailty is associated with adverse outcomes after kidney transplant. This systematic review suggests the importance of assessing frailty among kidney transplant candidates prior to transplantation. Further research focusing on pre-transplant assessment combined with frailty is warranted to improve kidney transplant management.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".