Paradigm Shift in the Surveillance and Management of Dysplasia in Inflammatory Bowel Disease (West)
Bibliographic record
Abstract
Patients with long-standing inflammatory bowel disease (IBD) colitis have a 2.4-fold higher risk of developing colorectal cancer (CRC) than the general population, for both ulcerative colitis (UC) and Crohn's disease (CD) colitis. Surveillance colonoscopy is recommended to detect early CRC and dysplasia. Most dysplasia discovered in patients with IBD is actually visible. Recently published SCENIC (Surveillance for Colorectal Endoscopic Neoplasia Detection and Management in Inflammatory Bowel Disease Patients: International Consensus Recommendations) consensus statements provide unifying recommendations for the optimal surveillance and management of dysplasia in IBD. SCENIC followed the prescribed processes for guideline development from the Institute of Medicine (USA), including systematic reviews, full synthesis of evidence and deliberations by panelists, and incorporation of the GRADE methodology. The new surveillance paradigm involves high-quality visual inspection of the mucosa, using chromoendoscopy and high-definition colonoscopy, with endoscopic recognition of colorectal dysplasia. Lesions are described according to a new classification, which replaces the term 'dysplasia associated lesion or mass (DALM)' and its derivatives. Targeted biopsies are subsequently done on areas suspicious for dysplasia, and resections are carried out for discrete, resectable lesions.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".