Organ donation in children: Role of the pediatric intensive care unit
Bibliographic record
Abstract
OBJECTIVES: Children waiting for organ transplants continue to die because of the shortage of available organs. Studies of organ donation in children are scarce. The evaluation of the organ donation experience in a pediatric tertiary care hospital may identify factors that influence actual organ donation rates and lead to strategies to improve pediatric organ donation. DESIGN: Retrospective study. SETTING: Pediatric intensive care unit in a Canadian pediatric referral center. PATIENTS: All children with brain death over an 8-yr period (1990-1997). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 199 children who fulfilled the criteria for brain death, 153 were medically suitable for organ donation. Families were approached for consent to organ donation in 128 (84%) of the 153 suitable cases. Consent was obtained in 63% (81/128) of those asked. Brain death caused by acute neurosurgical lesions was highly correlated to medical suitability and consent. Families identified as ethnic minorities were significantly more likely to refuse. After consent was granted, organs were procured from 63 (78%) of 81 donors, for an average of 3.6 organs transplanted per donor. There was a failure to procure organs in 22% (18/81) of cases after consent had been granted, primarily as a result of cardiocirculatory instability while in the intensive care unit. CONCLUSIONS: Despite an encouraging 63% consent rate for organ donation when families are approached, only 41% of potential donors proceeded to actual donation. Strategies for a prospective pediatric study should focus on mandatory request, multicultural issues, and aggressive postconsent medical management and procurement. The pivotal role of the pediatric intensive care unit practitioner should be emphasized.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".