How does initial treatment choice affect short‐term and long‐term costs for clinically localized prostate cancer?
Bibliographic record
Abstract
BACKGROUND: Data regarding costs of prostate cancer treatment are scarce. This study investigates how initial treatment choice affects short-term and long-term costs. METHODS: This retrospective, longitudinal cohort study followed prostate-cancer cases diagnosed in 2000 for 5 years using the Surveillance, Epidemiology, and End Results (SEER)-Medicare database. Men age≥66 years, in Medicare fee for service, diagnosed with clinically localized prostate cancer in 2000 while residing in a SEER region, were matched to noncancer controls using age, sex, race, region, comorbidity, and survival. On the basis of treatment received during the first 9 months postdiagnosis, patients were assigned to watchful waiting, radiation, hormonal therapy, hormonal+radiation, and surgery (may have received other treatments). Incremental costs for prostate cancer were the difference in costs for prostate cancer cases versus matched controls. Costs were divided into initial treatment (months -1 to 12), long-term (each 12 months thereafter), and total (months -1 to 60). Sensitivity analyses excluded the last 12 months of life. RESULTS: A total of 13,769 prostate-cancer cases were matched to 13,769 noncancer controls. Watchful waiting had the lowest initial treatment ($4270) and 5-year total costs ($9130). Initial treatment costs were highest for hormonal+radiation ($17,474) and surgery ($15,197). At $26,896, 5-year total costs were highest for hormonal therapy only followed by hormonal+radiation ($25,097) and surgery ($19,214). After excluding the last 12 months of life, total costs were highest for hormonal+radiation ($23,488) and hormonal therapy ($23,199). CONCLUSIONS: Patterns of costs vary widely based on initial treatment. These data can inform patients and clinicians considering treatment options and policy makers interested in patterns of costs.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".