Clinical Prediction Models of Patient and Graft Survival in Kidney Transplant Recipients: A Systematic Review.
Bibliographic record
Abstract
Background Identification of kidney transplant recipients (KTR) at higher risk for mortality and graft failure remains a challenge for the clinician. Several clinical prediction models (CPM) of patient and graft survival have been developed to help identify these individuals. Evaluation of the quality, validity and performance of these models is essential prior to clinical use. Purpose To systematically review CPM of patient and graft survival in KTR. Methods Medline and EMBASE were searched from 1966 to 2013 for English language articles. Eligible studies included CPM of patient and graft survival in adult &/or pediatric KTR with at least 100 patients. Data was extracted by 2 independent reviewers. Results A total of 11 studies were identified which included 3 studies (15 models) of patient survival, 9 studies (26 models) of graft survival in primarily deceased donor KTR and 2 studies (5 models) of graft survival in living donor recipients. Model discrimination was modest with c statistics of 0.60 to 0.75 for patient survival models, 0.61 to 0.90 for graft survival models in primarily deceased donor KTR and 0.71 to 0.88 for graft survival models in living donor recipients. Calibration was reported in 8 of the 11 studies and external validation of the models was done in 4 studies. One study met the criteria for clinical usefulness.Table: No Caption available.Conclusion The majority of existing CPM have modest discriminatory ability. Reporting of other measures of model performance is variable and external validation of models is inconsistent. Further study is needed to validate existing models &/or develop clinically useful prediction models.
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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 | 0.017 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 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".