Impact of Palliative Care Involvement on End-of-Life Care Patterns Among Adolescents and Young Adults With Cancer: A Population-Based Cohort Study
Bibliographic record
Abstract
PURPOSE: Evidence suggests that adolescents and young adults (AYAs) with cancer (defined as age 15-39 years) receive high-intensity (HI) medical care at the end-of-life (EOL). Previous population-level studies are limited and lack information on the impact of palliative care (PC) provision. We evaluated prevalence and predictors of HI-EOL care in AYAs with cancer in Ontario, Canada. A secondary aim was to evaluate the impact of PC physicians on the intensity of EOL care in AYAs. METHODS: A retrospective decedent cohort of AYAs with cancer who died between 2000 and 2017 in Ontario, Canada, was assembled using a provincial registry and linked to population-based health care data. On the basis of previous studies, the primary composite measure HI-EOL care included any of the following: intravenous chemotherapy < 14 days from death, more than one emergency department visit, and more than one hospitalization or intensive care unit admission < 30 days from death. Secondary measures included the most invasive (MI) EOL care (eg, mechanical ventilation < 14 days from death) and PC physician involvement. We determined predictors of outcomes using appropriate regression models. RESULTS: Of 7,122 AYAs, 43.8% experienced HI-EOL care. PC physician involvement (odds ratio [OR], 0.57; 95% CI, 0.51 to 0.63) and older age at death (OR, 0.60; 95% CI, 0.48 to 0.74) were associated with a lower risk of HI-EOL care. AYAs with hematologic malignancies were at highest risk for HI and MI-EOL care. PC physician involvement substantially reduced the odds of mechanical ventilation at EOL (OR, 0.36; 95% CI, 0.30 to 0.43). CONCLUSION: A large proportion of AYAs with cancer experience HI-EOL care. Our study provides strong evidence that PC physician involvement can help mitigate the risk of HI and MI-EOL care in AYAs with cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".