Differences in end‐of‐life communication for children with advanced cancer who were referred to a palliative care team
Bibliographic record
Abstract
BACKGROUND: There is a general consensus that involving a specialized palliative care team in the care of children with advanced cancer can help optimize end-of-life communication; however, how this compares to standard oncology care is still unknown. We aimed to determine whether there was an association between specialist palliative care involvement and improved end-of-life communication for children with advanced cancer and their families. PROCEDURE: We administered questionnaires to 75 bereaved parents (response rate 54%). Outcome measures were presence or absence of 11 elements related to end-of-life communication. RESULTS: Parents were significantly more likely to receive five communication elements if their child was referred to a palliative care team. These elements are: discussion of death and dying with parents by the healthcare team (P<0.01); discussion of death and dying with child by the healthcare team when appropriate (P < 0.01); providing parents with guidance on how to talk to their child about death and dying when appropriate (P < 0.01); preparing parents for medical aspects surrounding death (P = 0.02) and sibling support (P = 0.02). Children were less likely to be referred to a palliative care team if they had a hematologic malignancy. CONCLUSIONS: Children who receive standard oncology care are at higher risk of not receiving critical communication elements at end of life. Strategies to optimize end-of-life communication for children who are not referred to a palliative care team are needed.
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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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".