The economic burden of prostate cancer in Canada: forecasts from the Montreal Prostate Cancer Model
Bibliographic record
Abstract
BACKGROUND: We developed an economic model of prostate cancer management from diagnosis until death. We have used the Montreal Prostate Cancer Model to estimate the total economic burden of the disease in a cohort of Canadian men. METHODS: Using this Markov state-transition simulation model, we estimated the probability of prostate cancer, annual prostate cancer progression rates and associated direct medical costs according to patient age, tumour stage and grade, and treatment modalities in a 1997 cohort of Canadian men. The estimated lifetime costs of prostate cancer included the costs of clinical staging, initial treatments and complications, follow-up cancer therapies, routine outpatient care, and palliative care following metastatic disease. RESULTS: The clinical burden of prostate cancer forecasted using the model was similar to the projections of the National Cancer Institute. In the 1997 cohort of 5.8 million Canadian men between 40 and 80 years old, prostate cancer would be diagnosed in an estimated 701,491 men (12.1%) over their lifetime. Direct medical costs would total $9.76 billion, or $3.89 billion when discounted 5% annually. INTERPRETATION: The Montreal Prostate Cancer Model indicates that the economic burden of prostate cancer to Canada's health care system will be substantial. Further analyses are needed to identify the most efficient means of treating this disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".