The critically-ill pediatric hemato-oncology patient: epidemiology, management, and strategy of transfer to the pediatric intensive care unit
Bibliographic record
Abstract
Cancer is a leading cause of death in children. In the past decades, there has been a marked increase in overall survival of children with cancer. However, children whose treatment includes hematopoietic stem cell transplantation still represent a subpopulation with a higher risk of mortality. These improvements in mortality are accompanied by an increase in complications, such as respiratory and cardiovascular insufficiencies as well as neurological problems that may require an admission to the pediatric intensive care unit where most supportive therapies can be provided. It has been shown that ventilatory and cardiovascular support along with renal replacement therapy can benefit pediatric hemato-oncology patients if promptly established. Even if admissions of these patients are not considered futile anymore, they still raise sensitive questions, including ethical issues. To support the discussion and potentially facilitate the decision-making process, we propose an algorithm that takes into account the reason for admission (surgical versus medical) and the hemato-oncological prognosis. The algorithm then leads to different types of admission: full-support admission, "pediatric intensive care unit trial" admission, intensive care with adapted level of support, and palliative intensive care. Throughout the process, maintaining a dialogue between the treating physicians, the paramedical staff, the child, and his parents is of paramount importance to optimize the care of these children with complex disease and evolving medical status.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".