Predictors of Specialized Pediatric Palliative Care Involvement and Impact on Patterns of End-of-Life Care in Children With Cancer
Bibliographic record
Abstract
Purpose The impact of specialized pediatric palliative care (SPPC) teams on patterns of end-of-life care is unknown. We sought to determine (1) which children with cancer access SPPC and (2) the impact of accessing SPPC on the risk of experiencing high-intensity end-of-life care (intensive care unit admission, mechanical ventilation, or in-hospital death). Methods Using a provincial childhood cancer registry, we assembled a retrospective cohort of Ontario children with cancer who died between 2000 and 2012 and received care through pediatric institutions with an SPPC team. Patients were linked to population-based administrative data capturing inpatient, outpatient, and emergency visits. Children were classified as having SPPC, general palliative care, or no palliative care on the basis of SPPC clinical databases, physician billing codes, or inpatient diagnosis codes. Results Of the 572 children, 166 (29%) received care from an SPPC team for at least 30 days before death, and 100 (17.5%) received general palliative care. SPPC involvement was significantly less likely for children with hematologic cancers (OR, 0.3; 95% CI, 0.3 to 0.4), living in the lowest income areas (OR, 0.4; 95% CI, 0.2 to 0.8), and living further from the treatment center (OR, 0.5; 95% CI, 0.4 to 0.5). SPPC was associated with a five-fold decrease in odds of intensive care unit admission (OR, 0.2; 95% CI, 0.1 to 0.4), whereas general palliative care had no impact. Similar associations were seen with all secondary indicators. Conclusion When available, SPPC, but not general palliative care, is associated with lower intensity care at the end of life for children with cancer. However, access remains uneven. These results provide the strongest evidence to date supporting the creation of SPPC teams.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".