Differences in Health Care Expenditures by Cancer Patients During Their Last Year of Life: A Registry-Based Study
Bibliographic record
Abstract
BACKGROUND: During the last year of life, persons with cancer should probably have similar care needs and costs, but studies suggest otherwise. METHODS: A study of direct medical costs (excluding costs for expensive prescription drugs) was performed based on registry data in Stockholm County, which covers 2.4 million inhabitants, for all deceased persons with cancer during 2015-2021. The data were mainly analyzed with the aid of multiple regression models, including Generalized Linear Models (GLMs). RESULTS: < 0.0001), and having a hematological malignancy. In a separate model, where those 5% with the highest costs were identified, these variables were strengthened. Sex and socio-economic groups on an area level had little or no significance. Systemic cancer treatments during the last month of life and acute hospitals as place of death had only a moderate impact on costs in adjusted models. CONCLUSIONS: Higher costs are mainly related to lower age, higher frailty risk and having a hematological malignancy, and the effects are both statistically and clinically significant despite the fact that expensive drugs were not included. On the other hand, the costs were mainly comparable in regard to sex or socio-economic factors, indicating equal care.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".