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 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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".