Population-Based Trends in Systemic Therapy Use and Cost for Cancer Patients in the Last Year of Life
Bibliographic record
Abstract
BACKGROUND: The use of systemic therapy near the end of life can expose cancer patients to severe toxicity for minimal survival gain and comes with a high cost. Early palliative care is recommended, but there is evidence that aggressive care remains common. To better understand those patterns, the present study set out to describe trends in systemic therapy use and cost for cancer patients in the last year of life. METHODS: Using the BC Cancer Registry, a retrospective population-based cohort of cancer decedents (2002-2007) was identified and linked to systemic therapy records. The outcomes of interest were any systemic therapy use and total systemic therapy costs during the last year of life. Multiple logistic regression (systemic therapy use) and generalized linear regression (costs) were conducted, adjusting for age, sex, and survival. Subgroup analyses were performed for patients with primary colorectal, lung, prostate, or breast cancer. RESULTS: From 2002 to 2007, use of systemic therapy in the last 12-4 months of life increased by 21% (95% ci: 10% to 33%); no significant change in use in the last 3 months of life was observed. Costs for both periods increased over time, by 48% (95% ci: 36% to 63%) and by 33% (95% ci: 19% to 49%) respectively. The trends varied across cancer sites, with the greatest increases being observed for lung and colorectal cancer patients. CONCLUSIONS: The use and costs of systemic therapy have generally been increasing, putting pressure on health care providers and payers, but the quality-of-life implications for patients must be better understood.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".