Modeling Population-Level Impacts of Cell-Free DNA Screening for Colorectal Cancer in Canada
Bibliographic record
Abstract
Importance: Cell-free DNA (cfDNA) testing is an emerging approach for colorectal cancer screening that has been approved in the US. The impact of cfDNA testing in the Canadian setting, assuming adherence mirroring prior real-world cfDNA work and assay performance from a Guardant Health study, is unknown. Objective: To estimate how cfDNA screening impacts clinical and economic outcomes in Canada compared with existing screening approaches (fecal immunochemical testing [FIT] or colonoscopies). Design, Setting, and Participants: The OncoSim-Colorectal model (version 3.6.5.7) was used to simulate participation, relative effectiveness, and cost of introduction of cfDNA tests every 3 years. A population of 32 million Canadians were simulated and examined for outcomes and costs between 2024 and 2092. Exposures: Screening with colonoscopy, FIT, or cfDNA. Main Outcomes and Measures: Screen-detected colorectal cancer cases, deaths, health-adjusted person-years, potential years of life lost, and cost of cancer screening and management were examined. Results: Under higher participation, cfDNA detected 393 087 cases of colorectal cancer between 2024 and 2092 compared with 156 009 cases in the FIT scenario, and cfDNA reduced overall mortality by 121 383 deaths compared with current predictions with FIT. Linear regression models indicated that approximately 78% participation with 80% adherence or 69% participation with 100% adherence to cfDNA screening would be required to reduce deaths below the levels achieved by colonoscopy testing. Higher costs were associated with cfDNA testing, where each health-adjusted person-year had a cost of CAD $234.80 (US $164.06), and 0.025 deaths were averted per CAD $100 000 (US $69 874) additional dollars spent compared with FIT testing. When cfDNA testing was modeled with the same participation as FIT testing (43%), there was worse overall population impact (eg, greater number of deaths), emphasizing the importance of high participation for cfDNA testing to improve outcomes. Conclusions and Relevance: This study suggests that cfDNA testing could result in increased detection of colorectal cancer and reduced mortality if higher participation than reported in previous studies is achieved at the population level. Patient input on acceptance of blood-based vs stool-based screening may help inform real-world implementation.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 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".