Colorectal Cancer Surveillance in Patients with Inflammatory Bowel Disease and Primary Sclerosing Cholangitis
Bibliographic record
Abstract
BACKGROUND: The cost-effectiveness of annual colonoscopy for detection of colorectal neoplasia among patients with inflammatory bowel disease (IBD) and primary sclerosing cholangitis (PSC) is uncertain. The aim of this study was to determine whether annual colonoscopy among patients with IBD-PSC is cost-effective compared with less frequent intervals from the perspective of a publicly funded health care system. METHODS: A cost-utility analysis using a Markov model was used to simulate a 35-year-old patient with a 10-year history of well-controlled IBD and a recent diagnosis of concomitant PSC. The following strategies were compared: no surveillance, colonoscopy every 5 years, biennial colonoscopy, and annual colonoscopy. Outcome measures included: costs, number of cases of dysplasia found, number of cancers found and missed, deaths, quality-adjusted life-years (QALYs) gained, and the incremental cost per QALY gained. RESULTS: In the base-case analysis, no surveillance was the least expensive and least effective strategy. Compared with no surveillance, the cost per QALY of surveillance every 5 years was CAD $15,021. The cost per QALY of biennial surveillance compared with surveillance every 5 years was CAD $37,522. Annual surveillance was more effective than biennial surveillance, but at an incremental cost of CAD $174,650 per QALY gained compared with biennial surveillance. CONCLUSIONS: More frequent colonoscopy screening intervals improve effectiveness (i.e., detects more cancers and prevents additional deaths), but at higher cost. Health systems must consider the opportunity costs associated with different surveillance colonoscopy intervals when deciding which strategy to implement among patients with IBD-PSC.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".