Quality of Surveillance Impacts the Colitis-Associated Advanced Neoplasia Risk: A Multicenter Case-Control Study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Although colorectal cancer (CRC) surveillance is embedded in clinical inflammatory bowel disease (IBD) practice, a subset of patients still develops advanced neoplasia (AN) (high-grade dysplasia [HGD] and/or CRC). We aimed to assess the impact of surveillance quality on AN risk in IBD. METHODS: In this multicenter case-control study, we searched the Dutch nationwide pathology databank to identify IBD cases with AN and controls with indefinite or low-grade dysplasia. The surveillance colonoscopy preceding the index lesion (first indefinite for dysplasia [IND]/low-grade dysplasia [LGD] or AN) was used to assess the impact of surveillance quality. We assessed intervals, bowel preparation, cecal intubation, and absence of inflammation as primary quality indicators. In addition, we assessed chromoendoscopy, endoscopist expertise, hospital setting, and biopsy strategy. Associations of quality indicators with AN risk were determined with multivariable logistic regression analyses with Firth's correction. RESULTS: We included 137 cases and 138 controls. Delayed intervals (58.2% vs 39.6%) and active inflammation (65.3% vs 41.8%) were frequently present in cases and controls and were associated with AN (delayed interval: adjusted odds ratio [aOR], 2.00; 95% confidence interval [CI], 1.07-3.81; P = .03; active inflammation: aOR, 2.46; 95% CI, 1.33-4.61; P < .01). Surveillance compliant with primary quality indicators was associated with a reduced AN risk (aOR, 0.43; 95% CI, 0.22-0.91; P = .03), similar to chromoendoscopy (OR, 0.11; 95% CI, 0.01-0.89; P = .01). Other indicators were not significantly associated with AN. CONCLUSIONS: Surveillance compliant with primary quality indicators is associated with a reduced colitis-associated AN risk. Delayed surveillance intervals and active inflammation were associated with an increased AN risk. This underlines the importance of procedural quality, including endoscopic remission to optimize the effectiveness of endoscopic surveillance.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".