Evaluation of Transplant Candidates With a History of Nonadherence: An Opinion Piece
Bibliographic record
Abstract
The transparency and validity of assessing the candidacy of a patient for a solid organ transplant have recently been called into question by the media.1 Transplant centers perform a lengthy medical, surgical, and psychosocial evaluation on candidates with organ failure. This process ensures that there are no contraindications to surgery and to the use of long-term immunosuppressive medications. In addition, it helps ascertain if transplant candidates are likely to benefit from the transplant surgery over long term. Pretransplant nonadherence to prescribed therapy, medications, investigations, and clinical visits, is recognized as a relative or an absolute contraindication by transplant programs. For example, the Canadian Society of Transplantation states, given the use of immunosuppressive agents with a narrow therapeutic window, the impact of nonadherence to therapy on the risk of acute rejection and premature graft loss, and the scarcity of donor organs, nonadherence is a contraindication to kidney transplantation.2 This echoes the recommendations of some of the other major transplantation societies as summarized in Table 1.2-8 Most recommend delaying transplant surgery until patients have demonstrated adherence to therapy for 6 months. Table 1. Current Guidelines in the Assessment of Transplant Candidates With a History of Nonadherence.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".