Palliative Care Involvement and End-of-Life Care Intensity Among Adolescents and Young Adults with Nonmalignant Illnesses: A Population-Based Cohort Study in Ontario, Canada
Bibliographic record
Abstract
Background: Adolescents and young adults (AYAs) with life-limiting illnesses face unique challenges and often receive late or no palliative care (PC). This study examines the correlation between PC involvement and the intensity of end-of-life care among AYAs with nonmalignant life-limiting illnesses. Design: A retrospective cohort study analyzing population-based health care data from 2010 to 2018. Setting/Subjects: The study population included AYAs aged 15–39 who died in Ontario, Canada, from nonmalignant life-limiting illnesses during the study period ( n = 2313). Measurements: PC involvement was defined as at least one encounter with a PC provider. End-of-life (EOL) care intensity was measured using rates of emergency department visits, hospitalizations, intensive care unit admissions, and mechanical ventilation in the last 30 days of life. Results: Of the 2313 AYAs studied, 37.5% had at least one PC encounter during their lifetime. Specialist PC delivered ≥90 days before death was associated with lower intensity of EOL care, including fewer intensive care unit deaths (17% vs. 34% versus 31%, p < 0.0001) and emergency department visits (17% vs. 27% versus 21%, p = 0.0091) when compared to generalist PC and no PC, respectively. Conclusions: AYAs with nonmalignant illnesses received high EOL care intensity and had a high percentage of death in acute care settings. Specialist PC involvement was associated with improved EOL care outcomes compared with generalist and no PC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".