Palliative care involvement and intensity of end-of-life care among adolescents and young adults with cancer: a population-based study
Bibliographic record
Abstract
Background: Adolescents and young adults (AYAs) with cancer often experience high-intensity end-of-life care and low utilization of palliative care. To explore this further, we evaluated the frequency of palliative care involvement and its association with end-of-life care intensity among AYAs with cancer in Ontario, Canada. Methods: We conducted a retrospective cohort study using health administrative databases in Ontario, Canada, from Jan. 1, 2018, to Dec. 31, 2022. The cohort included AYA cancer decedents, aged 15 to 39 years. We categorized palliative care involvement into lifetime involvement and involvement in the last 90 days of life. We classified palliative care according to whether it was provided by a generalist or specialist physician using previously validated criteria. The primary outcome was the prevalence of palliative care involvement. Secondary outcomes included various measures of the intensity of end-of-life care; we analyzed whether palliative care involvement was associated with intensity of end-of-life care. Results: Among 1981 AYAs, 76% had palliative care involvement in the last 90 days of life, of which 89% were from specialist palliative care physicians. Specialist palliative care involvement was associated with higher rates of hospital admission (58% v. 57% v. 47%, p = 0.0004), but lower use of mechanical ventilation (12% v. 36% v. 33%, p < 0.0001), hospital deaths (42% v. 64% v. 56%, p < 0.0001), and intensive care unit deaths (12% v. 38% v. 38%, p < 0.0001) compared with generalist and no palliative care, respectively. Interpretation: Both palliative care and intensive care are increasingly used among AYAs with cancer at the end of life in Ontario, Canada. Increasing the availability of specialized AYA-focused palliative care may help to optimize end-of-life care in this population.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".