Eligibility for the kidney transplant wait list: a model for conceptualizing patient risk
Bibliographic record
Abstract
BACKGROUND: Determining eligibility for a kidney transplant is one of the most important decisions facing nephrologists. It is assumed that the harm of kidney transplantation is minimal and most will benefit. The purpose of this study was to quantify the probability of 'no benefit' as defined by death on the wait list; 'harm', defined by the probability that a transplanted patient would live less than the average wait listed patient; and 'benefit' for the probability a transplanted patient would outlive the average wait listed patient. METHODS: A computerized model was developed to replicate observed patient survival outcomes in deceased donor kidney transplantation. Three sequential periods of risk for the transplanted recipient compared to the wait listed cohort (increased, equivalent and reduced risk) were modeled. RESULTS: The model predicted that wait listed patients with a baseline mortality of 28 deaths per 100 patient years were equally likely to benefit or be harmed with a transplant. However if 20% of patients on the wait list were on hold (assuming a 2.2-fold higher mortality than those who were transplanted), then the baseline mortality rate for equal harm or benefit decreases to 22 deaths per 100 patient years (equivalent life expectancy 4.5 years). CONCLUSION: Patients with limited life expectancies are more likely to suffer some harm than derive benefit from kidney transplantation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".