Understanding the Effects of Competition for Constrained Colonoscopy Services with the Introduction of Population-level Colorectal Cancer Screening
Bibliographic record
Abstract
BACKGROUND: Median wait times for gastroenterology services in Canada exceed consensus-recommended targets and have worsened substantially over the past decade. Meanwhile, efforts to control colorectal cancer have shifted their focus to screening asymptomatic, average-risk individuals. Along with increasing prevalence of colorectal cancer due to an aging population, screening programs are expected to add substantially to the existing burden on colonoscopy services, and create competition for limited services among individuals of varying risk. Failure to understand the effects of operational programmatic screening decisions may cause unintended harm to both screening participants and higher-risk patients, make inefficient use of limited health care resources, and ultimately hinder a program's success. METHODS: We present a new simulation model (Simulation of Cancer Outcomes for Planning Exercises, or SCOPE) for colorectal cancer screening which, unlike many other colorectal cancer screening models, reflects the effects of competition for limited colonoscopy services between patient groups and can be used to guide planning to ensure adequate resource allocation. We include verification and validation results for the SCOPE model. RESULTS: A discrete event simulation model was developed based on an epidemiological representation of colorectal cancer in a sample population. Colonoscopy service and screening modules were added to allow observation of screening scenarios and resource considerations. The model reproduces population-based data on prevalence of colorectal cancer by stage, and mortality by cause of death, age, and sex, and attendant demand and wait times for colonoscopy services. CONCLUSIONS: The study model differs from existing screening models in that it explicitly considers the colonoscopy resource implications of screening activities and the impact of constrained resources on screening effectiveness.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.005 | 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".