Access to the Kidney Transplant Wait List
Bibliographic record
Abstract
The study examines selection for kidney transplantation and determines who are referred, how many had contraindications and whether comorbidity indices predict transplant status. Of 113 consecutive adult incident end-stage renal disease (ESRD) patients at this single center 47 (41.6%) were referred. Using published guidelines, 48 (42.5%) had a specific contraindication. However 26 (23%) were neither referred nor had contraindications. An ESRD mortality score, acute renal failure status and albumin were independent predictors of referral but only the mortality score was predictive of contraindication status. The Charlson and ESRD comorbidity indices were less predictive of contraindication or referral status. In a comparison of patients who were Candidates (referred and no contraindication, n = 39) compared to those who were Neither (not referred and no contraindications, n = 26), age was the most discriminating factor (c = 0.99, 95% CI 0.97-1.00). Comorbidity and mortality indices were inferior. Neither patients were older (75 +/- 7 years) and had comorbidity scores that were higher than Candidates but similar to those with contraindications (ESRD index; Neither 3.3 +/- 2.5, Candidate 1.4 +/- 1.8, and contraindication 4.1 +/- 3.4). Comorbitity indices do not help explain selection practices whereas age is an important discriminator. How many Neither patients would benefit from transplantation is not known.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".