Healthcare Cost Trajectories in the Last 2 Years of Life Among Patients With a Solid Metastatic Cancer: A Prospective Cohort Study
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Bibliographic record
Abstract
BACKGROUND: Most studies describe the "average healthcare cost trend" among patients with cancer. We aimed to delineate heterogeneous trajectories of healthcare cost during the last 2 years of life of patients with a metastatic cancer and to assess the associated sociodemographic and clinical characteristics and healthcare use. PATIENTS AND METHODS: We analyzed a sample of 353 deceased patients from a cohort of 600 with a solid metastatic cancer in Singapore, and we used group-based trajectory modeling to identify trajectories of total healthcare cost during the last 2 years of life. RESULTS: The average cost trend showed that mean monthly healthcare cost increased from SGD $3,997 during the last 2 years of life to SGD $7,516 during the last month of life (USD $1 = SGD $1.35). Group-based trajectory modeling identified 4 distinct trajectories: (1) low and steadily decreasing cost (13%); (2) steeply increasing cost in the last year of life (14%); (3) high and steadily increasing cost (57%); and (4) steeply increasing cost before the last year of life (16%). Compared with the low and steadily decreasing cost trajectory, patients with private health insurance (β [SE], 0.75 [0.37]; P=.04) and a greater preference for life extension (β [SE], -0.14 [0.07]; P=.06) were more likely to follow the high and steadily increasing cost trajectory. Patients in the low and steadily decreasing cost trajectory were most likely to have used palliative care (62%) and to die in a hospice (27%), whereas those in the steeply increasing cost before the last year of life trajectory were least likely to have used palliative care (14%) and most likely to die in a hospital (75%). CONCLUSIONS: The study quantifies healthcare cost and shows the variability in healthcare cost trajectories during the last 2 years of life. Policymakers, clinicians, patients, and families can use this information to better anticipate, budget, and manage healthcare costs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it