A200 HIGH-DEFINITION CHROMOENDOSCOPY RESULTS IN MORE SIGNIFICANT DYSPLASIA DETECTION THAN WHITE LIGHT ENDOSCOPY WITH RANDOM BIOPSIES IN ULCERATIVE COLITIS PATIENTS
Bibliographic record
Abstract
Abstract Background Ulcerative colitis (UC) is an inflammatory bowel disease that results in inflammation of the colonic mucosa, leading to abdominal pain, rectal bleeding, weight loss, and diarrhea. This chronic inflammation results in a 2.4-fold increased future risk of developing colorectal cancer (CRC) in UC patients compared to the general population. Thus, careful dysplasia screening modalities are required to prevent progression to CRC. Currently, both high-definition white light endoscopy with non-targeted biopsies (HD-WLR) and dye-spray chromoendoscopy (HDCE) are regularly used across Canada for dysplasia surveillance given existing research has been inconclusive regarding superiority of one particular method, and that recent guidelines do not suggest a preference. Purpose The primary objective of this study was to determine which surveillance modality yielded a higher dysplasia detection rate in UC patients, both by calculating the total number of dysplastic lesions detected, as well as calculating the number of patients with at least one dysplastic lesion detected using either technique. Method We conducted a single-centre retrospective chart review of 150 UC patients who underwent dysplasia surveillance at our site between January 2019-2021. We calculated the dysplasia detection rate of both techniques at the time of the first CRC screening colonoscopy. Result(s) Eighteen dysplastic lesions were detected in total, three by HD-WLR and fifteen by HDCE. Dysplasia was detected in 4% (3/75) and 14.5% (15/75) of UC patients by HD-WLR and HDCE respectively, with significantly fewer biopsies (4.44 + 4.3 vs 29.1 + 13.0) required. HD-WLR detected two polypoid and one non-polypoid lesion, while HDCE detected eleven polypoid and four non-polypoid lesions. No invisible dysplasia or colorectal cancer was detected. Screening was performed at 10.8 + 4.8 and 9.72 + 3.05 years following UC diagnosis for HDCE and HD-WLR respectively. Median withdrawal time was 9.0 + 2.7 min (HD-WLR) vs 9.6 + 3.9min (HDCE). Image Conclusion(s) HDCE resulted in higher dysplasia detection rates compared to HD-WLR in a UC patient population. Given the former technique is less tedious and costly, our findings suggest HDCE should be considered over HD-WLR for UC dysplasia surveillance. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".