Virtual scale endoscope versus snares for accuracy of size measurement of smaller colorectal polyps: a randomized controlled trial
Bibliographic record
Abstract
Background: Accurate measurement of polyp size during colonoscopy is crucial for informing clinical decisions such as resection technique and surveillance scheduling. This study aimed to compare the accuracy of polyp size measurement when using a virtual scale endoscope (VSE) or snare-based polyp size measurement. Methods: This randomized controlled trial enrolled 221 patients undergoing screening, surveillance, or diagnostic outpatient colonoscopies. Study subjects were randomized to have polyps detected during the colonoscopy measured for size either using the VSE or a snare of known size to estimate the size of each polyp. All polyps were measured for reference size directly after their removal from the colon using a digital caliper and before formalin fixation. Results: 93 polyps were included in the VSE group and 102 in the snare group. The VSE demonstrated significantly higher relative accuracy (80.0% [95%CI 77.0%–82.9%]) compared with snare-based size estimation (66.4% [95%CI 62.4%–70.5%]; P < 0.001). Misclassification rates were lower with the VSE for polyps >2 mm (13.1% vs. 39.3%) and >3 mm (22.6% vs. 55.4%). For diminutive polyps, the VSE better prevented misclassification of >5 mm polyps as 1–5 mm (21.4% vs. 73.0%). The VSE also outperformed snare-based estimation in measuring within 10% of the reference standard size (30.1% vs. 18.6%) and had lower rates of size underestimation (36.5% vs. 65.7%). Conclusions: Using the VSE improves the accuracy of polyp size measurement during colonoscopy in comparison with snare-based size estimation. In clinical scenarios, the VSE reduced misclassifications at clinically relevant size thresholds 2, 3, and 5 mm, which is relevant for the correct choice of polypectomy technique or when implementing resect-and-discard strategies.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".