Expert endoscopist assessment of colorectal polyp size using virtual scale endoscopy, visual or snare-based estimation: a prospective video-based study
Bibliographic record
Abstract
Background and aims Accurate polyp size estimation during colonoscopy has an impact on clinical decision-making. A laser-based virtual scale endoscope (VSE) is available to allow measuring polyp size using a virtual adaptive scale. This study evaluates video-based polyp size measurement accuracy among expert endoscopists using either VSE or visual assessment (VA) with either snare as reference size or without any reference size information.Methods A prospective, video-based study was conducted with 10 expert endoscopists. Video sequences from 90 polyps with known reference size (fresh specimen measured using calipers) were distributed on three different slide sets so that each slide set showed the same polyp only once with either VSE, VA or snare-based information. A slide set was randomly assigned to each endoscopist. Endoscopists were asked to provide size estimation based on video review.Results Relative accuracies for VSE, VA, and snare-based estimation were 75.1% (95% CI [71.6–78.5]), 65.0% (95% CI [59.5–70.4]) and 62.0% (95% CI [54.8–69.0]), respectively. VSE yielded significantly higher relative accuracy compared to VA (p = 0.002) and to snare (p = 0.001). A significantly lower percentage of polyps 1–5 mm were misclassified as >5 mm using VSE versus VA and snare (6.52% vs. 19.6% and 17.5%, p = 0.004) and a significantly lower percentage of polyps >5 mm were misclassified as 1–5 mm using VSE versus VA and snare (11.4% vs. 31.9% and 14.9%, p = 0.038).Conclusions Endoscopists estimate polyp size with the highest accuracy when virtual adaptive scale information is displayed. Using a snare to assist sizing did not improve measurement accuracy compared to displaying visual information alone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".