Using the Cancer Risk Management Model to Evaluate Colorectal Cancer Screening Options for Canada
Bibliographic record
Abstract
BACKGROUND: Several screening methods for colorectal cancer (crc) are available, and some have been shown by randomized trials to be effective. In the present study, we used a well-developed population health simulation model to compare the risks and benefits of a variety of screening scenarios. Tests considered were the fecal occult blood test (fobt), the fecal immunochemical test (fit), flexible sigmoidoscopy, and colonoscopy. Outcomes considered included years of life gained, crc cases and deaths prevented, and direct health system costs. METHODS: A natural history model of crc was implemented and calibrated to specified targets within the framework of the Cancer Risk Management Model (crmm) from the Canadian Partnership Against Cancer. The crmm-crc permits users to enter their own parameter values or to use program-specified base values. For each of 23 screening scenarios, we used the crmm-crc to run 10 million replicate simulations. RESULTS: Using base parameter values and some user-specified values in the crmm-crc, and comparing our screening scenarios with no screening, all screening scenarios were found to reduce the incidence of and mortality from crc. The fobt was the least effective test; it was not associated with lower net cost. Colonoscopy screening was the most effective test; it had net costs comparable to those for several other strategies considered, but required more than 3 times the colonoscopy resources needed by other approaches. After colonoscopy, strategies based on the fit were predicted to be the most effective. In sensitivity analyses performed for the fobt and fit screening strategies, fobt parameter values associated with high-sensitivity formulations were associated with a substantial increase in test effectiveness. The fit was more cost-effective at the 50 ng/mL threshold than at the 100 ng/mL threshold. CONCLUSIONS: The crmm-crc provides a sophisticated and flexible environment in which to evaluate crc control options. All screening scenarios considered in this study effectively reduced crc mortality, although sensitivity analyses demonstrated some uncertainty in the magnitude of the improvements. Where possible, local data should be used to reduce uncertainty in the parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".