Recognition of Microscopic Colitis at Colonoscopy
Bibliographic record
Abstract
Microscopic colitis is a common condition in Canada and is characterized by watery diarrhea with normal endoscopic mucosal appearance using conventional white-light endoscopy. Two types of microscopic colitis are recognized by histological appearance: lymphocytic colitis and collagenous colitis. Recent studies suggest that 10% to 30% of older patients who present with diarrhea and demonstrate normal mucosal appearance at endoscopy have microscopic colitis (1). Increasingly, however, with high-definition endoscopy assisted by postprocessing of images by filter techniques in combination with chromoendoscopy, subtle abnormalities of the mucosa are apparent in patients with microscopic colitis. iScan (Pentax, Japan) is a newly developed postprocessing light filter that enhances details of the mucosal surface, mucosal patterns (i-Scan p designated iScan 1) and vessel architecture (i-Scan v designated iScan 2). i-Scan is a technique designed for differentiating neoplastic from non-neoplastic lesions in the colon. This new digital modality can characterize mucosal patterns of the gastrointestinal mucosa in detail. Subtle abnormalities can, therefore, be recognized as either neoplastic or inflammatory. Hoffman et al (2) have tested the efficacy of high-definition endoscopy alone in comparison with i-Scan or chromoendoscopy with methylene blue (0.1%) in screening for colorectal cancer. They demonstrated that both i-Scan and chromoendoscopy identified more lesions compared with high-definition endoscopy alone. i-Scan was also able to predict neoplasia as precisely as chromoendoscopy, with an accuracy of 89% to 97%. Case Presentation A 50-year-old woman with persistent undiagnosed chronic watery diarrhea underwent colonoscopy, with random biopsies that were negative. Another colonoscopy was performed with iScan + chromoendoscopy with 0.2% indigo carmine to enhance mucosal surfaces and patterns, and to target biopsies in the colon. iScan 1 and iScan 2 showed irregularity of the colonic mucosal pattern, although the mucosa appeared normal with white-light endoscopy. iScan + chromoendoscopy with indigo carmine characterized, in detail, the mucosal pattern as nodularmosaic with a small honeycomb pit-pattern appearance (Figure 1). Such an appearance is characteristic of microscopic colitis. Targeted
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".