Outcomes following heart or bilateral‐lung transplantation from donors who died of drug toxicity in British Columbia, Canada
Bibliographic record
Abstract
INTRODUCTION: The illicit drug toxicity (overdose) crisis has worsened across Canada; between 2016 and 2021, more than 28,000 individuals have died of drug toxicity. Organ donation from persons who experience drug toxicity death (DTD) has increased in recent years. This study examines whether survival after heart or bilateral-lung transplantation differed by donor cause of death. METHODS: We studied transplant recipients in British Columbia who received heart (N = 110) or bilateral-lung (N = 223) transplantation from deceased donors aged 12-70 years between 2013 and 2019. Transplant recipient survival was compared by donor cause of death from drug toxicity or other. Five-year Kaplan-Meier estimates of survival and 3-year inverse probability treatment weighted Cox proportional hazards models were conducted. RESULTS: DTD donors made up 36% (40/110) of heart and 24% (54/223) of bilateral-lung transplantations. DTD donors were more likely to be young, white, and male. Unadjusted 5-year recipient survival was similar by donor cause of death (heart: 87% for DTD and 86% for non-DTD, p = .75; bilateral- lung: 80% for DTD and 76% for non-DTD, p = .65). Adjusted risk of mortality at 3-years post-transplant was similar between recipients of DTD and non-DTD donor heart (hazard ratio [HR]: .94, 95% confidence interval (CI): .22-4.07, p = .938) and bilateral-lung (HR: 1.06, 95% CI: .41-2.70, p = .908). CONCLUSION: Recipient survival after heart or bilateral-lung transplantation from DTD donors and non-DTD donors was similar. Donation from DTD donors is safe and should be considered more broadly to increase organ donation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".