Intensity of end-of-life (EOL) cancer care in Western Washington (WA) versus Alberta (AB), Canada (CA).
Bibliographic record
Abstract
89 Background: Aggressive care at the EOL may lead to unnecessary suffering and healthcare costs for patients (pts) with cancer. Despite similar populations and state-of-the-art cancer delivery systems, we hypothesize that EOL care may be more intense in the United States (US) multi-payer system vs the CA single-payer system. Using cancer registry and claims data, we compared EOL cancer care between WA and AB. Methods: Adult pts with AJCC stage II-IV solid tumors who died between 2014 and 2016 were identified from regional population-based cancer registries in WA and AB. Data sources were 1) WA State Cancer Registry (WSCR) and Western WA Cancer Surveillance System (CSS) linked to enrollment files and claims from four regional insurers and 2) CA National Ambulatory Care Reporting System (NACRS), Discharge Abstracts Database (DAD), and CT records from AB Health Services. Proportions of pts receiving chemotherapy (CT), ICU admission, or > 1 ED visit in the last 30 days of life (DOL) in WA and AB were determined and compared using two sample z-test with two-tailed hypothesis (α = 0.05). Results: 11,177 AB and 7,906 WA pts met study inclusion criteria. Median age was 71 (IQR 61-79) and 75 (IQR 68-82) for AB and WA, respectively. The most common cancer types represented include lung (31% AB; 35% WA), colorectal (17% AB; 9% WA), breast (10% AB; 6% WA) and prostate (11% AB; 4% WA). A similar proportion of pts in WA and AB experienced multiple ED visits in the last 30 DOL (12.4% WA vs 12.1% AB). CT use in the last 14 and 30 DOL was greater in WA vs AB (6.3% and 13.4% vs 2.7% and 6.6%, respectively) and ICU admissions in the last 30 DOL were substantially greater in WA vs AB (19.9% vs 3.9%). Conclusions: CT use and ICU admissions in the last 30 DOL were more common in WA than AB. The lower rate of ICU admissions in AB may be due to a provincial effort to prioritize goals of care discussions . Future studies to characterize and compare drivers of inappropriately aggressive EOL care may help improve cancer care for patients (pts) in the US and AB. [Table: see text]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".