Cost-effectiveness of surveillance after curative resection (CR) of metastatic colorectal cancer (CRC).
Bibliographic record
Abstract
526 Background: Surveillance after CR of stage I-III CRC is recommended by most major oncology organizations to detect asymptomatic recurrences. Such recurrences are more likely to benefit from early interventions such as CR of metastases. Only the NCCN recommends a surveillance schedule after CR of metastases that includes CEA testing, imaging and clinical evaluation every 3-6 months for 2 years, and then every 6-12 months in years 3 to 5. Periodic endoscopy is also recommended. It is unclear if there is cost-effective surveillance strategy for metastatic CRC after CR. Methods: A Monte Carlo micro-simulation model was constructed using a 1-month cycle length and 10 year time horizon. Surveillance strategies were compared based on NCCN guidelines, with testing every 3 months (3M) or 6 months (6M), as well as two alternate strategies of testing every 12 months (12M) or no surveillance (None) for 5 years. Recurrence, repeat CR rates, and survival outcomes were modeled from population-based outcomes of 257 patients who had CR of mCRC in British Columbia, Canada. Asymptomatic recurrences were more likely to undergo CR, compared to symptomatic ones. Additional costs, utilities, and probabilities were derived from the literature. Costs are in 2015 CAD and utilities in Quality-adjusted life years (QALY), and both discounted at 3% and half-cycle corrected. Analyses were performed using TreeAge Pro with 1000 trials and 1000 distribution samplings. Results: The incremental cost-effectiveness ratio (ICER) and net monetary benefit (NMB) are listed in the Table. Increasing the frequency of surveillance tests does lead to modest gains in QALY, however, the cost of surveillance and subsequent treatment is high. Using a willingness to pay threshold of 150 000 CAD, the 6M strategy would be favored. Conclusions: In the Canadian context, the optimal surveillance strategy after CR of mCRC matches with the 6M strategy recommended by the NCCN. An additional Canadian data set will be used to externally validate the model outcomes. [Table: see text]
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".