Organ Donation Following Neurologic and Circulatory Determination of Death
Bibliographic record
Abstract
OBJECTIVES: To describe important considerations during the process of caring for critically ill children who may be potential organ donors and supporting the family during the death of their child. DESIGN: Literature review and expert commentary. MEASUREMENT AND MAIN RESULTS: Medical literature focusing on pediatric donation, best pediatric donation practices, donor management, and factors influencing donation were reviewed. Additional pediatric data were obtained and reviewed from the U.S. Organ Procurement and Transplantation Network. Achieving successful organ donation requires the coordinated efforts of the critical care team, organ donation organization, and transplant team to effectively manage a potential donor and recover suitable organs for transplantation. Collaboration between these teams is essential to ensure that all potential organs are recovered in optimal condition, to reduce death and morbidity in children on transplantation waiting lists as well as fulfilling the family's wishes for their dying child to become a donor. CONCLUSIONS: Organ donation is an important component of end-of-life care and can help the healing process for families and medical staff following the death of a child. The process of pediatric organ donation requires healthcare providers to actively work to preserve the option of donation before the death of the child and ensure donation occurs after consent/authorization has been obtained from the family. Medical management of the pediatric organ donor requires the expertise of a multidisciplinary medical team skilled in the unique needs of caring for children after neurologic determination of death and those who become donors following circulatory death after withdrawal of life-sustaining medical therapies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".