The impact on clinical outcomes and healthcare resources from discontinuing colonoscopy surveillance subsequent to low-risk adenoma removal: A simulation study using the OncoSim-Colorectal model
Bibliographic record
Abstract
OBJECTIVE: To estimate the impact on clinical outcomes and healthcare resource use from recommending that patients with 1-2 low-risk adenomas (LRAs) return to routine fecal immunochemical test (FIT) screening instead of surveillance colonoscopy, from a Canadian provincial healthcare system perspective. METHODS: The OncoSim-Colorectal microsimulation model simulated average-risk individuals eligible for FIT-based colorectal cancer (CRC) screening in Alberta, Canada. We simulated two surveillance strategies that applied to individuals with 1-2 LRAs (<10 mm) removed as part of the average risk CRC screening program: (a) Surveillance colonoscopy (status quo) and (b) return to FIT screening (new strategy); both at 5 years after polypectomy. A 75 ng/mL FIT positivity threshold was used in the base case. The simulations projected average annual CRC outcomes and healthcare resource use from 2023 to 2042. We conducted alternative scenarios and sensitivity analyses on key variables. RESULTS: Returning to FIT screening (versus surveillance colonoscopy) after polypectomy was projected to have minimal impact on long-term CRC incidence and deaths (not statistically significant). There was a projected decrease of one (4%) major bleeding event and seven (5%) perforation events per year. There was a projected increase of 4800 (1.5%) FIT screens, decrease of 3900 (5.1%) colonoscopies, and a decrease of $3.4 million (1.2%) in total healthcare costs per year, on average. The annual colonoscopies averted and healthcare cost savings increased over time. Results were similar in the alternative scenarios and sensitivity analyses. CONCLUSIONS: Returning to FIT screening would have similar clinical outcomes as surveillance colonoscopy but could reduce colonoscopy demand and healthcare costs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".