High-definition chromoendoscopy results in more significant dysplasia detection than white light endoscopy with random biopsies in ulcerative colitis patients: A single-center retrospective study
Bibliographic record
Abstract
The goal of this study was to determine whether high-definition white light endoscopy with random biopsies (HD-WLR) or chromoendoscopy (HDCE) yielded a higher dysplasia detection rate in ulcerative colitis patients. Ulcerative colitis (UC) patients have a 2.4-fold increased future risk of developing colorectal cancer compared to the general population and require careful dysplasia screening modalities. Both HD-WLR and HDCE are regularly used, and recent guidelines do not suggest a preference. UC patients who underwent dysplasia surveillance at our site between January 2019 and 2021 were retrospectively reviewed. We calculated the dysplasia detection rate of both techniques at the first CRC screening colonoscopy. Eighteen dysplastic lesions were detected in total, 3 by HD-WLR and fifteen by HDCE. Dysplasia was detected in 4% (3/75) and 20% (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 using the former. HD-WLR detected 2 polypoid and one non-polypoid lesion, while HDCE detected eleven polypoid and 4 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 minutes (HD-WLR) vs 9.6 + 3.9 minutes (HDCE). HDCE is associated with higher dysplasia detection rates compared to HD-WLR in a UC patient population. Given the former technique is less tedious and costly, our findings complement existing studies that suggest HDCE may be considered over HD-WLR for UC dysplasia 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".