A Canado-European external validation of the Kidney Transplant Failure Score
Bibliographic record
Abstract
Abstract In kidney transplantation, obtaining early information about the risk of graft failure helps physicians and patients anticipate a potential return to dialysis or retransplantation. Clinical prediction models are commonly used to obtain such risk estimation, but their performance needs to be continuously evaluated in various contexts. We propose an external validation study of the Kidney Transplant Failure Score in a pooled sample of 3,144 patients transplanted between 2010 and 2015 in France, Belgium, Norway and Canada. This score is used at the first transplantation anniversary to predict the probability of graft failure over the following seven years. The target population was defined as adult recipients of a kidney from a neurologically deceased donor without graft failure in the first year post-transplantation. Graft failure was defined as a return to dialysis. Around 10% of patients returned to dialysis, and 12.6% died during the seven-year follow-up. The KTFS authors fitted a Cox model and then adjusted its coefficients to maximize the discrimination, yielding the KTFS final version. We evaluated the performance of the initial and final versions of the KTFS, as well as the performance of another model we developed to consider death as a competing event. All KTFS versions yielded similarly good discrimination (area under the time-dependant receiver operating curve around from 0.79 [0.76-0.82] to 0.80 [0.77-0.84]), while the discrimination-optimized one presented important miscalibration. Clinical utility, assessed through net benefit, was also the lowest for the discrimination-optimized version. Our results warn against using the current KTFS version and recommend using either the initial coefficients or the competing risk-based ones instead. Lay summary French nephrologists have used the Kidney Transplant Failure Score (KTFS) for nearly fifteen years to predict kidney graft failure eight years after the transplantation. Because predictive performance decreases over time, we first verified that the score could still predict correctly in France and also in other countries. Then, we compared the different KTFS formulas to find that the one currently used is suboptimal and should be avoided. Our findings show that the KTFS is still a reliable source of information for both kidney recipients and nephrologists when using its first version.
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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 | 0.039 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".