Contraindications to kidney transplantation: uneven grounds?
Bibliographic record
Abstract
BACKGROUND: Determining eligibility for a kidney transplant is an important decision. Practice guidelines define contraindications to transplantation; however many are not evidence based. Canadian guidelines recommend that patients unlikely to survive the wait period not be evaluated. The purpose of this study was to evaluate what proportion of patients with a contraindication would survive the wait time. METHODS: Consecutive incident dialysis patients (January 2006 to December 2012) with a contraindication, defined using Canadian guidelines, were studied. Mortality rates were determined for each individual contraindication. Theoretical survival to the median wait time to transplantation was calculated. RESULTS: Of 746 incident patients, 435 (58 %) were deemed to have a contraindication at dialysis start. Nearly 80 % had a contraindication with a high mortality rate (dementia, multisystem disease, etc.). Patients with high mortality rates were less likely to survive the wait list than be transplanted. Patients with non-adherence, obesity, and potentially reversible disease had relatively low mortality rates, were more likely to survive, and possibly be transplanted at a time with the prospect of a better outcome. CONCLUSIONS: This study gives some credence that many patients with a contraindication are not likely to benefit. A better framework of defining contraindications is needed to allow better decision-making.
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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 | 0.011 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".