Organ Donation after Circulatory Determination of Death: Lessons and Unresolved Controversies
Bibliographic record
Abstract
The several articles in this special issue on organ donation after circulatory determination of death or, as it is often put, donation after cardiac death (DCD), draw lessons from different kinds of experience in order to guide efforts in the U.S. to develop or refine policies for DCD. One lesson comes from a major and, by many measures, successful experimental DCD program in Washington, D.C. in the 1990s. Another lesson comes from European countries that have adopted presumed-consent legislation, a form of “opt out” that facilitates DCD as well as donation after neurological determination of death (DND). Another lesson, from the perspective of critical care medicine in Canada, attends to the implications of viewing a dying patient, undergoing resuscitative procedures, as a potential organ donor. A final lesson sketches implications of legislation and court cases in the U.S., often involving DND, for initiating temporary organ preservation (TOP) in DCD programs before consent has been obtained for organ donation. Some of these lessons are optimistic about the prospects for DCD, especially if certain steps are taken, while others are more cautious, particularly because of the costs and risks involved in DCD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.012 | 0.030 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.023 | 0.037 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".