The Use of Donation After Circulatory Death Organs for Simultaneous Liver-kidney Transplant: To DCD or Not to DCD?
Bibliographic record
Abstract
BACKGROUND: Because of the challenges with organ scarcity, many centers performing simultaneous liver-kidney transplant (SLKT) are opting to accept donation after circulatory death (DCD) organs as a means of facilitating earlier transplant and reducing death rates on the waitlist. It has been suggested, however, that DCD organs may have inferior graft and patient survival posttransplant compared with donation after neurologic death (DND) organs. METHODS: We created a Markov model to compare the overall outcomes of accepting a DCD SLKT now versus waiting for a DND SLKT in patients waitlisted for SLKT, stratified by base Model for End-Stage Liver Disease (MELD) score (≤20, 21-30, >30). RESULTS: Waiting for DND SLKT was the preferred treatment strategy for patients with a MELD score of 30 or less (incremental value of 0.54 and 0.36 quality-adjusted life years for MELD score of 20 or less and MELD score of 21 to 30 with DND versus DCD SLKT, respectively). The option to accept a DCD SLKT became the preferred choice for those with a MELD score greater than 30 (incremental value of 0.31 quality-adjusted life years for DCD versus DND SLKT). This finding was confirmed in a probabilistic sensitivity analysis and persisted when analyzing total life years obtained for accept DCD versus do not accept DCD. CONCLUSIONS: There is a benefit to accepting DCD SLKT for patients with MELD score greater than 30. Although not accepting DCD SLKT and waiting for DND SLKT is the preferred option for patients with MELD of 30 or less, the incremental value is small.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".