Hospice Care and Survival among Elderly Patients with Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Recently observed trends toward increasingly aggressive end-of-life care may reflect providers' concerns that hospice may hasten death. METHODS: Using the Surveillance, Epidemiology, and End Results-Medicare linked database, we identified 7879 patients aged 65 years or older who died of advanced non-small-cell lung cancer from 1991 through 1999 after surviving for at least 3 months from their cancer diagnoses. Length of hospice admission post-cancer diagnosis and indicators of aggressive end-of-life care were ascertained based on claims data. We evaluated overall survival and care near death after controlling for baseline characteristics by using propensity score (PS) and instrumental variable analyses (IVA). RESULTS: Hospice patients were older, more likely to be non-Hispanic white and female, more likely to reside in urban areas with high hospice availability and higher socioeconomic status, more likely to be treated in a teaching hospital, and received less aggressive end-of-life care compared to nonhospice patients. Among hospice patients, those experiencing short-term hospice admissions within 3 days of death were more likely to be male, reside in urban areas, be treated in a teaching hospital, and receive more aggressive end-of-life care. PS analysis found that survival favored hospice patients slightly relative to nonhospice patients by 5.0 percentage points (25.7% versus 20.7%) at 1 year and 1.4 percentage points (6.9% versus 5.5%) at 2 years postdiagnosis (p < 0.001), while there was no significant difference between those with short- and longer duration hospice stays (p = 1.00). IVA confirmed these findings. CONCLUSIONS: Hospice enrollment did not compromise length of survival following advanced lung cancer diagnosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".