Hospice Use Among Patients With Lymphoma: Impact of Disease Aggressiveness and Curability
Bibliographic record
Abstract
BACKGROUND: Little is known about factors that influence hospice use for patients with blood cancers. We aimed to characterize hospice enrollment in a large population of patients with B-cell non-Hodgkin lymphoma (NHL) and assess the impact of disease characteristics such as aggressiveness and curability. METHODS: Using the Surveillance, Epidemiology, and End Results-Medicare database, we identified patients age 65 years and older who were diagnosed with indolent NHL, aggressive NHL, or mantle cell lymphoma (MCL, which is aggressive and incurable) and died between 1999 and 2009. We determined the prevalence of hospice use and predictors thereof, using multivariable logistic regression. All statistical tests were two-sided. RESULTS: Of 18 777 patients, 9645 had indolent NHL, 8226 had aggressive NHL, and 906 had MCL. Of the total cohort, 41.6% enrolled in hospice, and 34.3% enrolled three or more days before death. Compared with patients with indolent NHL, those with MCL were more likely to enroll (adjusted odds ratio [AOR] = 1.72, 95% confidence interval [CI] = 1.49 to 1.98), followed by patients with aggressive NHL (AOR = 1.41, 95% CI = 1.32 to 1.50). Other factors statistically significantly associated with hospice use included older age, female sex, white race, high socioeconomic status, and later year of death. CONCLUSIONS: In this large cohort of patients with lymphoma, hospice use was substantially lower than the national average for all cancers, suggesting either the need for improvement in enrollment or that the current hospice model is not meeting this population's end-of-life needs. Moreover, the fact that patients with MCL were most likely to enroll suggests that the end-of-life phase may be more easily determined in the context of cancers that are both aggressive and incurable.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".