Increased rate of deceased donor liver transplantation for candidates willing to receive organs from donors with human immunodeficiency virus
Bibliographic record
Abstract
Historically, liver transplant (LT) candidates with human immunodeficiency virus (HIV) have experienced high waitlist mortality. Since the HIV Organ Policy Equity (HOPE) Act expands access to organs from donors with HIV, we assessed the impact of HOPE on LT rate and wait time for this population. We linked data from a multicenter HOPE in Action study to Scientific Registry of Transplant Recipients (February 21, 2019 to June 1, 2024) and used Poisson regression to compare transplant rates among 99 candidates willing to accept HOPE donors (HOPE candidates) to 13 495 candidates with or without HIV not listed as willing to accept HOPE donors (non-HOPE candidates) matched on transplant center. The median time to any deceased donor liver transplant (DDLT) was 2.3 months for HOPE and 1.1 years for non-HOPE candidates. Within 2 years of listing, 90.9% of HOPE versus 58.5% of non-HOPE candidates received a DDLT (P < .001). HOPE was associated with an overall 3.11-fold higher DDLT incident rate ratio (95% CI 2.48-3.88, P < .001). Stratified by model for end-stage liver disease score categories 6 to 14, 15 to 24, 25 to 34, and 35 to 40/status 1; HOPE candidates had 10.12-fold, 5.31-fold, 1.41-fold and 2.90-fold higher DDLT rates, respectively. Willingness to accept livers from donors with HIV improves access to liver transplantation for candidates with HIV.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".