Decision-Making in the Era of New Medical Technologies in Pediatric Hematology-Oncology: The Death of Palliative Care?
Bibliographic record
Abstract
BACKGROUND: Recent advances in immunology, genomics, and cellular therapy have opened numerous therapeutic possibilities in pediatric hematology-oncology, generating new hope in poor prognosis situations. How decisions are made when it comes to treatments and aims needs to be explored in this new technologic context. In particular, their impact on the gold standard of early referral to palliative care must be assessed. MATERIALS AND METHODS: Stemming from an ethnographic study combining semistructured interviews and observations carried out in a hematopoietic stem cell transplant unit in a Montréal Pediatric Hospital, we discuss the decision-making process when a patient faces poor prognosis. RESULTS AND DISCUSSION: Although health care providers individually envisioned that palliative care may be the best course of action for patients receiving emergent therapy, they remained collectively in the curative mode. The intricate relationship between science, hope, caregiver, and care receiver sustains this perspective even when (near) death is the probable outcome. When proven treatment fails, emerging therapeutic possibilities offer new hope that can delay the referral to the palliative care team.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".