Incidence and Distribution of Transplantable Organs from Donors after Circulatory Determination of Death in U.S. Intensive Care Units
Bibliographic record
Abstract
RATIONALE: All U.S. acute care hospitals must maintain protocols for recovering organs from donors after circulatory determination of death (DCDD), but the numbers, types, and whereabouts of available organs are unknown. OBJECTIVES: To assess the maximal potential supply and distribution of DCDD organs in U.S. intensive care units. METHODS: We conducted a population-based cohort study among a randomly selected sample of 50 acute care hospitals in the highest-volume donor service area in the United States. We identified all potentially eligible donors dying within 90 minutes of the withdrawal of life-sustaining therapy from July 1, 2008 to June 30, 2009. MEASUREMENTS AND MAIN RESULTS: Using prespecified criteria, potential donors were categorized as optimal, suboptimal, or ineligible to donate their lungs, kidneys, pancreas, or liver. If only optimal DCDD organs were used, the deceased donor supplies of these organs could increase by up to 22.7, 8.9, 7.4, and 3.3%, respectively. If optimal and suboptimal DCDD organs were used, the corresponding supply increases could be up to 50.0, 19.7, 18.5, and 10.9%. Three-quarters of DCDD organs could be recovered from the 17.2% of hospitals with the highest annual donor volumes-typically those with trauma centers and more than 20 intensive care unit beds. CONCLUSIONS: Universal identification and referral of DCDD could increase the supply of transplantable lungs by up to one-half, and would not increase any other organ supply by more than one-fifth. The marked clustering of DCDD among a small number of identifiable hospitals could guide targeted interventions to improve DCDD identification, referral, and management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".