Predictors of specialized pediatric palliative care involvement and impact on patterns of end-of-life care in children with cancer: A population-based study.
Bibliographic record
Abstract
10573 Background: Children with cancer are at risk of receiving high-intensity (HI) care at the end-of-life (EOL) and associated high symptom burden. The impact of palliative care (PC) delivered by generalists or of specialized pediatric palliative care (SPPC) on patterns of EOL care is unknown, with previous studies limited by small sample sizes or low response rates. Methods: Using a provincial registry, we assembled a retrospective cohort of Ontario children with cancer who died between 2000-2012 and who received care through a pediatric institution with a SPPC team and a clinical PC database. Patients were linked to population-based healthcare data capturing inpatient, outpatient, and emergency visits. Clinical PC databases were used to identify patients receiving SPPC. Remaining patients were categorized as having received either general PC (GPC) or no PC depending on the presence of PC associated physician billing or inpatient codes. We determined predictors of SPPC involvement, and whether either SPPC or GPC was associated with HI-EOL outcomes: ICU admission < 30 days from death, mechanical ventilation < 14 days from death, or in hospital death. Sensitivity analyses excluded treatment-related mortality (TRM) cases. Results: 572 patients met inclusion criteria. Children less likely to receive SPPC services included those with hematologic cancers [odds ratio (OR) 0.33, 95th confidence interval (CI) 0.30-0.37; p < 0.001)], in the lowest income quintile (OR 0.44, 95CI 0.23-0.81; p = 0.009), and living at increased distance from the treatment center (OR 0.46, 95CI 0.40-0.52; p < 0.0001). In multivariate analysis, SPPC was associated with a 3-fold decrease in the odds of an EOL ICU admission (OR 0.32, 95CI 0.18-0.57), while GPC had no impact. Similar associations were seen with all other HI-EOL indicators. Excluding TRM had little impact. Conclusions: SPPC, but not GPC, is associated with lower intensity care at EOL. Access to such care however remains uneven. In the absence of randomized trials, these results provide the strongest evidence to date supporting the creation of SPPC teams. These results can be used to support PC advocacy and policy efforts.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".