Variation in practice patterns and outcomes across United Network for Organ Sharing allocation regions
Bibliographic record
Abstract
BACKGROUND: The number of heart transplants performed is limited by organ availability and is managed by the United Network for Organ Sharing (UNOS). Efforts are underway to make organ disbursement more equitable as demand increases. HYPOTHESIS: Significant variation exists in contemporary patterns of care, wait times, and outcomes among patients undergoing heart transplantation across UNOS regions. METHODS: We identified adult patients undergoing first, single-organ heart transplantation between January 2006 and December 2014 in the UNOS dataset and compared sociodemographic and clinical profiles, wait times, use of mechanical circulatory support (MCS), status at time of transplantation, and 1-year survival across UNOS regions. RESULTS: We analyzed 17 096 patients undergoing heart transplantation. There were no differences in age, sex, renal function, and peripheral vascular resistance across regions; however, there was 3-fold variation in median wait time (range, 48-166 days) across UNOS regions. Proportion of patients undergoing transplantation with status 1A ranged from 36% to 79% across regions (P < 0.01), and percentage of patients hospitalized at time of transplantation varied from 41% to 98%. There was also marked variation in MCS and inotrope utilization (28%-57% and 25%-58%, respectively; P < 0.001). Durable ventricular assist device implantation varied from 20% to 44% (P < 0.001), and intra-aortic balloon pump utilization ranged from 4% to 18%. CONCLUSIONS: Marked differences exist in patterns of care across UNOS regions that generally trend with differences in waitlist time. Novel policy initiatives are required to address disparities in access to allografts and ensure equitable and efficient allocation of organs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".