Measuring Size of Colorectal Polyps Using a Virtual Scale Endoscope or Visual Assessment: A Randomized Controlled Trial
Bibliographic record
Abstract
INTRODUCTION: This study aimed to compare the accuracy of polyp size measurements using a virtual scale endoscope (VSE) with an integrated laser-based adaptive scale function and visual assessment (VA) during colonoscopies. METHODS: We conducted a single-blinded, prospective randomized controlled trial. Eligible patients (aged 45-80 years) undergoing screening, surveillance, or diagnostic colonoscopies were randomly assigned (1:1) into 2 groups. In the intervention group, all detected polyps were measured for size using VSE; in the control group, all polyps were measured using VA. Size measurements were compared with a reference standard of digital caliper measurement immediately post polypectomy. The primary outcome was the relative accuracy of real-time VSE measurement compared with VA. Secondary outcomes included the mean differences and the correlations between VSE or VA sizes and the reference standard of measurement. RESULTS: Overall, 230 patients were enrolled and randomized. The relative size measurement accuracy of VSE was 84% in 118 polyps, which was significantly higher than that of VA (105 polyps; 68.4%, P < 0.001). VSE resulted in a significantly higher percentage of size measurements within 25% of true size compared with VA (81.4% vs 41%, P < 0.001). VSE had a significantly lower percentage for >5-mm polyps incorrectly sized as 1-5 mm compared with VA (13.5% vs 57.1%; P < 0.001) and a significantly lower percentage for >3-mm polyps incorrectly sized as 1-3 mm compared with VA (11.3% vs 56.5%; P < 0.001). DISCUSSION: VSE significantly improves the size measurement accuracy of colorectal polyps during colonoscopies compared with VA and results in fewer misclassifications at relevant decision-making size thresholds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".