Cost-Effectiveness of Surveillance after Metastasectomy of Stage IV Colorectal Cancer
Bibliographic record
Abstract
Surveillance of stage IV colorectal cancer (CRC) after curative-intent metastasectomy can be effective for detecting asymptomatic recurrence. Guidelines for various forms of surveillance exist but are supported by limited evidence. We aimed to determine the most cost-effective strategy for surveillance following curative-intent metastasectomy of stage IV CRC. We performed a decision analysis to compare four active surveillance strategies involving clinic visits and investigations elicited from National Comprehensive Cancer Network (NCCN) recommendations. Markov model inputs included data from a population-based cohort and literature-derived costs, utilities, and probabilities. The primary outcomes were costs (2021 Canadian dollars) and quality-adjusted life years (QALYs) gained. Over a 10-year base-case time horizon, surveillance with follow-ups every 12 months for 5 years was most economically favourable at a willingness-to-pay threshold of CAD 50,000 per QALY. These patterns were generally robust in the sensitivity analysis. A more intensive surveillance strategy was only favourable with a much higher willingness-to-pay threshold of approximately CAD 425,000 per QALY, with follow-ups every 3 months for 2 years then every 12 months for 3 additional years. Our findings are consistent with NCCN guidelines and justify the need for additional research to determine the impact of surveillance on CRC outcomes.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".