Lung cancer costs by treatment strategy and phase of care among patients enrolled in Medicare
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: We studied trends in lung cancer treatment cost over time by phase of care, treatment strategy, age, stage at diagnosis, and histology. METHODS: Using the Surveillance, Epidemiology, and End Results (SEER)-Medicare database for years 1998-2013, we allocated total and patient-liability costs into the following phases of care for 145 988 lung cancer patients: prediagnosis, staging, surgery, initial, continuing, and terminal. Patients served as self-controls to determine cancer-attributable costs based on individual precancer diagnosis healthcare costs. We fit linear regression models to determine cost by age and calendar year for each stage at diagnosis, histology, and treatment strategy and presented all costs in 2017 US dollars. RESULTS: Monthly healthcare costs prior to lung cancer diagnosis were $861 for a 70 years old in 2017 and rose by an average of $17 per year (P < 0.001). Surgery in 2017 cost $30 096, decreasing by $257 per year (P = 0.007). Chemotherapy and radiation costs remained stable or increased for most stage and histology groups, ranging from $4242 to $8287 per month during the initial six months of care. Costs during the final six months of life decreased for those who died of lung cancer or other causes. CONCLUSIONS: Cost-effectiveness analyses of lung cancer control interventions in the United States have been using outdated and incomplete treatment cost estimates. Our cost estimates enable updated cost-effectiveness analyses to determine the benefit of lung cancer control from a health economics point of view.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it