The Effect of the Opioid Epidemic on Donation After Circulatory Death Transplantation Outcomes
Bibliographic record
Abstract
BACKGROUND: The opioid epidemic and the deaths of otherwise healthy individuals due to drug overdose in the United States has major implications for transplantation. The current extent and safety of utilization of liver and kidney grafts from donation after circulatory death (DCD) donors who died from opioid overdose is unknown. METHODS: Using national data from 2006 to 2016, we estimated the cumulative incidence of graft failure for recipients of DCD grafts, comparing the risk among recipients of organs from donors who died of anoxic drug overdose and recipients of organs from donors who died of other causes. RESULTS: One hundred seventy-nine (6.2%) of 2908 liver graft recipients and 944 (6.1%) of 15520 kidney graft recipients received grafts from donors who died of anoxic drug overdose. Grafts from anoxic drug overdose donors were less frequently used compared with other DCD grafts (liver, 25.9% versus 29.6%; 95% confidence interval [CI] for difference, -6.7% to -0.7%; kidney, 81.0% versus 84.7%; 95% CI for difference, -7.3% to -0.1%). However, the risk of graft failure at 5 years was similar for recipients of anoxic drug overdose donor grafts and recipients of other grafts (liver risk difference, 1.8%; 95% CI, -7.8% to 11.8%; kidney risk difference, -1.5%; 95% CI, -5.4% to 3.1%). CONCLUSIONS: In the context of the current opioid epidemic, utilization of anoxic drug overdose DCD donor grafts does not increase the risk of graft failure and may help to address waitlist demands.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".