Locus‐of‐care disparities in end‐of‐life care intensity among adolescents and young adults with cancer: A population‐based study using the IMPACT cohort
Bibliographic record
Abstract
BACKGROUND: Adolescents and young adults (AYAs) with cancer may experience elevated rates of high-intensity end-of-life (HI-EOL) care. Locus-of-care (LOC) disparities (pediatric vs adult) in AYA end-of-life (EOL) care are unstudied. METHODS: A decedent population-based cohort of Ontario AYAs diagnosed between 1992 and 2012 at the ages of 15 to 21 years was linked to administrative data. The authors determined the prevalence and associations of a composite outcome of HI-EOL care that included any of the following: intravenous chemotherapy within 14 days of death, more than 1 emergency department visit, more than 1 hospitalization, or an intensive care unit (ICU) admission within 30 days of death. Secondary outcomes included measures of the most invasive EOL care (ventilation within 14 days of death and ICU death) and in-hospital death. RESULTS: There were 483 decedents: 60.5% experienced HI-EOL care, 20.3% were ventilated, and 22.8% died in the ICU. Compared with patients with solid tumors, patients with hematological malignancies had the greatest odds of HI-EOL care (odds ratio [OR], 2.3; 95% confidence interval [CI], 1.5-3.4), ventilation (OR, 4.7; 95% CI, 2.7-8.3), and ICU death (OR, 4.4; 95% CI, 2.6-4.4). Subjects treated in pediatric centers versus adult centers near death (OR, 2.4; 95% CI, 1.2-4.8) and those living in rural areas (OR, 2.1; 95% CI, 1.1-3.9) were more likely to experience ICU death. CONCLUSIONS: AYAs with cancer experience high rates of HI-EOL care, with patients in pediatric centers and those living in rural areas having the highest odds of ICU death. This study is the first to identify LOC-based disparities in EOL care for AYAs, and it highlights the need to explore the mechanisms underlying these disparities.
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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 | 0.001 |
| 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".