Accuracy in Polyp Size Measurement Among Surgeons, Gastroenterologists, Trainees, and Experts: A Prospective Video-Based Study
Bibliographic record
Abstract
INTRODUCTION: Polyp size determination plays an important role in endoscopic decision making and follow-up determination. However, there is a lack of knowledge of endoscopist accuracy for polyp sizing and efficacy of available tools for size measurement. Our aim was to compare the accuracy of visual assessment, snare, forceps, and virtual scale endoscope (VSE) in estimating polyp size among a diverse group of endoscopists. METHODS: We conducted a prospective video-based study. One hundred twenty polyps measured and recorded along with all available measurement tools were randomized to visual assessment, snare, forceps, or VSE group. Eleven endoscopists conducted video-based measurement using the randomized measurement tool. Primary outcome was relative accuracy in polyp size measurement compared with caliper measurement immediately postresection. RESULTS: One thousand three hundred twenty measurements were performed. VSE had statistically significantly higher relative accuracy when compared to forceps (79.3 vs 71.3%; P < 0.0001). Forceps had statistically significantly higher relative accuracy when compared to visual assessment (71.3 vs 63.6%; P = 0.0036). There was no statistically significant difference when comparing visual assessment and snare-based measurements (63.6 vs 62.8%; P = 0.797). Overall, 21.5% of polyps >5 mm were misclassified as ≤5 mm and 17.3% of polyps ≥10 mm were misclassified as <10 mm. VSE had the lowest percentage of polyps >5 mm misclassified as ≤5 mm (2.6%), polyps ≤5 mm misclassified as >5 mm (5.1%), and polyps <10 mm misclassified as ≥10 mm (1.7%). DISCUSSION: Visual size estimation of polyps is inaccurate independently of training level, sex, and specialty. Size measurement accuracy can be improved using forceps and yields the highest relative accuracy when an adaptive scale technology is used.
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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 | 0.006 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".