Effect of organ donation after circulatory determination of death on number of organ transplants from donors with neurologic determination of death
Bibliographic record
Abstract
BACKGROUND: To increase the available pool of organ donors, Ontario introduced donation after circulatory determination of death (DCD) in 2006. Other jurisdictions have reported a decrease in donations involving neurologic determination of death (NDD) after implementation of DCD, with a drop in organ yield and quality. In this study, we examined the effect of DCD on overall transplant activity in Ontario. METHODS: We examined deceased donor and organ transplant activity during 3 distinct 4-year eras: pre-DCD (2002/03 to 2005/06), early DCD (2006/07 to 2009/10) and recent DCD (2010/11 to 2013/14). We compared these donor groups by categorical characteristics. RESULTS: Donation increased by 57%, from 578 donors in the pre-DCD era to 905 donors in the recent DCD era, with a 21% proportion (190/905) of DCD donors in the recent DCD era. However, overall NDD donation also increased. The mean length of hospital stay before declaration for NDD was 2.7 days versus 6.0 days before withdrawal of life support and subsequent asystole in cases of DCD. The average organ yield was 3.73 with NDD donation versus 2.58 with DCD (p < 0.001). Apart from hearts, all organs from DCD donors were successfully transplanted. From the pre-DCD era to the recent DCD era, transplant activity in each era increased for all solid-organ recipients, including heart (from 158 to 216), kidney (from 821 to 1321), liver (from 477 to 657) and lung (from 160 to 305). INTERPRETATION: Implementation of DCD in Ontario led to increased transplant activity for all solid-organ recipients. There was no evidence that the use of DCD was pre-empting potential NDD donation. In contrast to groups receiving other organs, heart transplant candidates have not yet benefited from DCD.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".