Impact of Follow-Up Colonoscopy Quality on Canadian Colorectal Cancer Outcomes and Costs
Bibliographic record
Abstract
Background: Most colorectal cancer (CRC) cases develop from precancerous polyps. Screening using fecal testing for occult blood, with follow-up diagnostic colonoscopy to remove polyps, can prevent invasive cancer from occurring. However, there is variation in the quality of colonoscopy, which may result in nonoptimal health outcomes. Aim: We evaluated the impact of follow-up colonoscopy quality on health outcomes, resource utilization and costs using the OncoSim-CRC microsimulation model (version 2.5). Methods: OncoSim is a microsimulation model led by the Canadian Partnership Against Cancer with model development by Statistics Canada. We compared results of high quality follow-up colonoscopy after positive fecal immunochemical testing (FIT) (colonoscopy sensitivity for cancer detection= 95%; compliance to follow-up colonoscopy = 85%) with that of reduced quality colonoscopy. Variations in colonoscopy performance were simulated through plausible overall effectiveness reduction (ER) and incomplete colonoscopy (IC). Screening system/patient follow-up deficiencies were simulated through poor compliance to diagnostic colonoscopy (PC). Modeling assumptions included: Biennial FIT screening of average-risk people aged 50-74; positive FIT followed by diagnostic colonoscopy; ER = 20% reduction in overall sensitivity; IC = zero sensitivity in proximal colon; PC = compliance reduction by 50%. Overall cost was calculated for 2017-2036 in undiscounted 2016 CAD, and included screening, treatment and end-of-life costs. Results: Compared with high quality colonoscopy follow-up, incomplete colonoscopy with poor compliance over 20 years led to as many as 12% new cases of CRC; 23% more CRC deaths; 89% more interval cancers; and 6% increased costs to the health care system, annually. Conclusion: Reduced colonoscopy quality can lead to considerable declines in the predicted effectiveness of screening and to increased costs to the healthcare system. Efforts to increase and maintain colonoscopy performance is a necessary component of CRC control planning.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".