Eligibility for the Kidney Transplant Wait List: A Model for Conceptualizing Risk.
Bibliographic record
Abstract
Determining eligibility is one of the most important decisions facing nephrologists. It is assumed that the harm of kidney transplantation is minimal and most benefit. Some confusion has been generated by the belief that since all patient groups that are transplanted have a net benefit in life years gained (even age 70+ yrs), transplantation is being denied to many that would benefit. This study quantifies 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 (figure).Figure: No Caption available.The model assumed 3 periods of risk for the recipient compared to the wait listed cohort (increased, equivalent and reduced). All patients wait 2 years. With higher wait list mortality rates (Table) the proportion of patients who benefit falls. Patients with an annual mortality rate of >22 deaths per 100 pt yrs were more likely to be harmed (live less than the average wait listed patient) than benefit. At 25 deaths per 100 pt yrs subjects were 1.3 times more likely to have their life shortened than to achieve a longer life and 39% would die on the list.Table: No Caption available.Longer wait times >2 years increase the proportion that do not benefit and further reduce benefit. Although there is always a group of patients that dervive net benefit, patients with high mortality rates are more likely to have no benefit or have a slight reducion in overall survival after withstanding the trauma of surgery than benefit. There is wisdom in limiting kidney transplantation to patients with life expectancies >5 years (mortality rates of <20 deaths per 100 pt yrs).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".