Access to kidney transplantation: outcomes of the non-referred
Bibliographic record
Abstract
BACKGROUND: There is a concern that some, especially older people, are not referred and could benefit from transplantation. METHODS: We retrospectively examined consecutive incident end stage renal disease (ESRD) patients at our center from January 2006 to December 2009. At ESRD start, patients were classified into those with or without contraindications using Canadian eligibility criteria. Based on referral for transplantation, patients were grouped as CANDIDATE (no contraindication and referred), NEITHER (no contraindication and not referred) and CONTRAINDICATION. The Charlson Comorbidity Index (CCI) was used to assess comorbidity burden. RESULTS: Of the 437 patients, 133 (30.4%) were CANDIDATE (mean age 50 and CCI 3.0), 59 (13.5%) were NEITHER (age 76 and CCI 4.4), and 245 (56.1%) were CONTRAINDICATION (age 65 and CCI 5.5). Age was the best discriminator between NEITHER and CANDIDATES (c-statistic 0.96, P <0.0001) with CCI being less discriminative (0.692, P <0.001). CANDIDATES had excellent survival whereas those patients designated NEITHER and CONTRAINDICATION had high mortality rates. NEITHER patients died or developed a contraindication at very high rates. By 1.5 years 50% of the NEITHER patients were no longer eligible for a transplant. CONCLUSIONS: There exists a relatively small population of incident patients not referred who have no contraindications. These are older patients with significant comorbidity who have a small window of opportunity for 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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".