Beyond white light: optical enhancement in conjunction with magnification colonoscopy for the assessment of mucosal healing in ulcerative colitis
Bibliographic record
Abstract
Abstract Background and study aim The I-SCAN optical enhancement (OE) system with magnification is a recently introduced combination of optical and digital electronic virtual chromoendoscopy, which enhances mucosal and vascular details. The aim of this pilot study was to investigate the use of I-SCAN OE in the assessment of inflammatory changes in ulcerative colitis (UC). Patients and methods A total of 41 consecutive patients with UC and 9 control patients were examined by I-SCAN OE (Pentax Medical, Tokyo, Japan). Targeted biopsies of the imaged areas were obtained. A new optical enhancement score focusing on mucosal and vascular changes was developed. The diagnostic accuracy of I-SCAN OE was calculated against histology using two UC histological scores – Robarts Histopathology Index (RHI) and ECAP (Extent, Chronicity, Activity, Plus additional findings). Results The overall I-SCAN OE score correlated with ECAP (r = 0.70; P < 0.001). The accuracy of the overall I-SCAN OE score to detect abnormalities by ECAP was 80 % (sensitivity 78 %, specificity 100 %). I-SCAN OE vascular and mucosal scores correlated with ECAP (r = 0.65 and 0.71, respectively; P < 0.001). The correlation between overall I-SCAN OE score and RHI was r = 0.61 (P < 0.01), and the accuracy to detect abnormalities by RHI was 68 % (sensitivity 78 %, specificity 50 %). The majority of patients with Mayo 0 had abnormalities on I-SCAN OE. Conclusion In UC, the new I-SCAN OE technology accurately identified mucosal inflammation, and correlated well with histological scores of chronic and acute changes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".