Active surveillance for low-risk prostate cancer compared with immediate treatment: a Canadian cost comparison
Bibliographic record
Abstract
BACKGROUND: Clinical consequences of active surveillance compared with immediate treatment have been evaluated in patients with low-risk prostate cancer; yet, its financial benefits have not been adequately studied in Canada or elsewhere. Our study objective was to evaluate the direct costs associated with active surveillance and immediate treatment in the Canadian context. METHODS: We developed a Markov model with Monte Carlo microsimulations to estimate the Canadian cost of prostate cancer associated with immediate treatment and active surveillance strategies. The patients receiving active surveillance were assumed to receive delayed treatment at a rate of 8.35%, 4.17% and 2.1% per year for the first 2 years, years 3 to 5, and years 6 to 10 of follow-up, respectively. All costs were assigned in Canadian dollars and reflect Quebec's health system. RESULTS: With active surveillance, the mean cost of prostate cancer management over the first year and 5 years of follow-up was estimated at $6200 (95% confidence interval [CI] $6083-$6317) per patient. The mean cost corresponding to immediate treatment was estimated at $13 735 (95% CI $13 615-$13 855) per patient. We estimated that patients receiving active surveillance who received delayed treatment incurred higher costs of $16 257 per patient. INTERPRETATION: Active surveillance could offer important economic benefits to the Canadian health system, estimated at $96.1 million for each annual cohort of incident prostate cancer. These results add to the economic rationale advocating active surveillance for eligible men with low-risk prostate cancer.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".