End-of-Life Care for Lung Cancer Patients in the United States and Ontario
Bibliographic record
Abstract
BACKGROUND: Both the United States and Canada offer government-financed health insurance for the elderly, but few studies have compared care at the end of life for cancer patients between the two systems. METHODS: We identified care for non-small cell lung cancer (NSCLC) patients who died of cancer at age 65 years and older during 1999-2003. Patients were identified from US Surveillance, Epidemiology, and End Results (SEER)-Medicare data (N = 13,533) and the Ontario Cancer Registry (N = 8100). Health claims during the last 5 months of life identified chemotherapy and emergency room use, hospitalizations, and supportive care. We estimated rates per person-months (PM) for short-term survivors (died <6 months after diagnosis) and longer-term survivors (died ≥6 months after diagnosis), adjusting for demographic differences. To test whether monthly rates in Ontario were statistically significantly different from the United States, standardized differences were computed, and a 99% confidence interval (CI) was constructed to account for the multiple tests performed. All statistical tests were two-sided. RESULTS: Rates of chemotherapy use were statistically significantly higher for SEER-Medicare patients than Ontario patients in every month before death (short-term survivors at 5 months before death: SEER-Medicare, 33.2 patients per 100 PM vs Ontario, 9.5 per 100 PM, rate difference = 23.7 per 100 PM, 99% CI = 18.3 to 29.1 per 100 PM, P < .001; longer-term survivors at 5 months before death: SEER-Medicare, 24.4 patients per 100 PM vs Ontario, 14.5 per 100 PM, rate difference = 9.9 per 100 PM, 99% CI = 7.7 to 12.1 per 100 PM, P <. 001). During the last 30 days of life, fewer SEER-Medicare than Ontario patients were hospitalized (short-term survivors, 49.9 vs 78.6 patients per 100 PM, rate difference = 28.6 per 100 PM, 95% CI = 22.9 to 34.4 per 100 PM, P <. 001; longer-term survivors, 44.1 vs 67.1 patients per 100 PM, rate difference = 23.0 per 100 PM, 95% CI = 18.5 to 27.5 per 100 PM, P < .001). CONCLUSIONS: NSCLC patients in both Ontario and the United States used extensive end-of-life care. Limited availability of hospice care in Ontario and differing attitudes between the United States and Ontario regarding end-of-life care may explain the differences in practice patterns.
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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 | 0.003 |
| 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 | 0.001 |
| 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".