EOLP-05. POSTMORTEM TISSUE DONATION: GIVING FAMILIES THE ABILITY TO CHOOSE
Bibliographic record
Abstract
Abstract To our knowledge, Gift From a Child (GFAC) is the only dedicated consortium providing autopsy resources for children with brain tumors. Using the parental data and the experience of being a multi-institutional post mortem CNS tumor collection program, GFAC seeks to inform medical professionals of the need to approach families about post-mortem donation and demonstrate how barriers to donation can be overcome. Much like the one that occurred for organ donation, a cultural shift is needed with the goal to offer every family the option to donate. Collected survey data shows, 98% of families who donated were satisfied with their decision, compared to 20% of families who did not donate being satisfied with their decision. Most families want to be provided the opportunity to chooseto donate, citing the desire to advance research and help future families. For families who donated, over 71% initiated the donation conversation with their clinician. Among those who did not donate, 58% reported they had not been asked. The healthcare team initiated the donation conversation only 20% of the time for both groups. Without the conversation being initiated, many families remain unaware of the necessity and impact of donation on advancing research. The timing and structure of how donation is brought up impacts if families proceed with donating, however, data shows that withholding this option is frequently more distressing for families. Parent interviews and survey data indicate the majority of families are most open to this conversation once it’s evident a child will not survive and during transition to hospice care. Presentation donation as a driver for research, helping the next child, and as a key step in the family’s grieving process positively correlate with a family’s decision to donate.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.066 | 0.013 |
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".