Overcoming autopsy barriers in pediatric cancer research
Bibliographic record
Abstract
BACKGROUND: More than 13,000 children annually in the United States and Canada under the age of 20 will be diagnosed with cancer at a mortality approaching 20% 1,2. Tumor samples obtained by autopsy provide an innovative way to study tumor progression, potentially aiding in the discovery of new treatments and increased survival rates. The purpose of this study was to identify barriers to autopsies and develop guidelines for requesting autopsies for research purposes. PROCEDURE: Families of children treated for childhood cancer were referred by patient advocacy groups and surveyed about attitudes and experiences with research autopsies. From 60 interviews, barriers to autopsy and tumor banking were identified. An additional 14 interviews were conducted with medical and scientific experts. RESULTS: Ninety-three percent of parents of deceased children did or would have consented to a research autopsy if presented with the option; however, only half of these families were given the opportunity to donate autopsy tissue for research. The most significant barriers were the physicians' reluctance to ask a grieving family and lack of awareness about research opportunities. CONCLUSIONS: The value of donating tumor samples to research via an autopsy should be promoted to all groups managing pediatric cancer patients. Not only does autopsy tumor banking offer a potentially important medical and scientific impact, but the opportunity to contribute this Legacy Gift of autopsy tumor tissue also creates a positive outlet for the grieving family. Taking these findings into account, our multidisciplinary team has developed a curriculum addressing key barriers.
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 | 0.078 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| 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".