Participatory simulation modeling to inform colorectal cancer screening in a complex remote northern health system: Canada’s Northwest Territories
Bibliographic record
Abstract
BACKGROUND & AIMS: Colorectal cancer (CRC) mortality in the Northwest Territories (NWT), a northern region of Canada, could be reduced by implementing a CRC screening program. However, this may require additional colonoscopy resources. We used participatory simulation modeling to predict colonoscopy demand and to develop strategies for implementing a feasible and effective CRC screening program in this complex remote northern health system. METHODS: Using a participatory simulation modeling approach, we first developed a conceptual model of CRC screening with local collaborators. This approach informed our parameter adjustments of an existing microsimulation model, OncoSim-CRC, using data from a retrospective cohort review of CRC screening between 2014-2019 and secondary data. Model scenarios reflecting program implementation were run for 500 million cases. Validity was assessed, and outputs analyzed with collaborators. Alternative scenarios were developed to reduce colonoscopy demand and results were presented to end-users. RESULTS: We estimated that colonoscopy demand with a CRC screening program phased-in over 5 years would surpass capacity within 2 years. If demand is met, screen-detected cancers would increase by 110 %, and clinically-detected cases would reduce by 26 % over the next 30 years. We also found that prolonging the phase-in period, or revising adenoma follow-up guidelines would reduce colonoscopy demand while still improving cancer detection. Both strategies were considered feasible by collaborators. The adjusted model was valid, and the projections informed local end-users plans for CRC screening delivery. CONCLUSIONS: Using participatory simulation modeling, we projected that a screening program would improve CRC detection but surpass current colonoscopy capacity. Phasing-in the screening program and reducing endoscopic adenoma follow-up would enhance feasibility of a CRC screening program in the NWT and help maintain its effectiveness.
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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 | 0.001 |
| 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".