Moving Beyond the Perfect Donor
Bibliographic record
Abstract
BACKGROUND: Despite a growing demand for donor hearts, more than two-thirds of viable hearts are discarded in North America. Our initiative sought to improve donor heart utilization by reviewing institutional donor acceptance practices and implementing targeted interventions. OBJECTIVES: Our aim was to increase donor heart utilization rate to 20% and reduce the rate of missed viable opportunities to ≤10% within a 12-month period, and assess the incidence of primary graft dysfunction preintervention and postintervention. METHODS: This prospective single-center study included all donor heart offers from January 1, 2021, to December 31, 2023. Donor demographics, clinical data, and utilization outcomes were collected. Missed viable opportunities were defined by criteria including left ventricular ejection fraction >50%, age <45 years, ischemic time <4 hours, and ≤20% undersizing by predicted heart mass. In January 2023, we launched a dashboard to track and display real-time utilization rates, held a Vanguard international speaker series, and introduced cardiologist/surgeon donor report cards. RESULTS: Before the interventions (January 2021-December 2022), 54 of 436 (12.4%) donor offers were utilized; postintervention (January 2023-December 2023), 43 out of 233 offers (18.5%) were utilized (P = 0.038). Missed viable opportunities decreased from 19.3% (2021-2022) to 8.6% (2023) (P < 0.001). Additionally, there was a trend toward reduction in severe primary graft dysfunction (26% vs 12%, P = 0.121) postintervention. CONCLUSIONS: Our interdisciplinary, multipronged interventions doubled our donor heart utilization rate and significantly reduced missed viable opportunities without compromising short-term heart transplant recipient outcomes.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".