A123 COMPARING SIZE MEASUREMENT OF SIMULATED COLORECTAL POLYPS WHEN USING A NOVEL VIRTUAL SCALE ENDOSCOPE, ENDOSCOPIC RULER OR FORCEPS: A BLINDED RANDOMIZED TRIAL
Bibliographic record
Abstract
Abstract Background Accurate polyp size measurement is important for guideline conforming choice of polypectomy techniques and subsequent surveillance interval assignments. Some endoscopic tools (forceps or endoscopic rulers [ER]) exist to help with visual size estimation. A virtual scale endoscope (VSE) has been developed that allows superimposing a virtual measurement scale during live endoscopies. Purpose Our aim was to evaluate the performance of VSE when compared to ER and forceps-based measurement. Method We conducted a randomized trial to evaluate the relative accuracy of size measurement of simulated colorectal polyps when using: VSE, ER, and forceps. Six endoscopists performed 60 measurements randomized at a 1:1:1 ratio using each method. Primary outcome was relative accuracy in polyp size measurement. Secondary outcomes included misclassification of sizes at the 5, 10, and 20mm thresholds. Result(s) A total of 360 measurements were performed. The relative accuracy of biopsy forceps, ER, and VSE was 78.9% (95%CI=76.2-81.5), 78.4% (95%CI=76.0-80.8), and 82.7% (95%CI=80.8-84.8). VSE had significantly higher accuracy compared to biopsy forceps (p=0.02) and ER (p=0.006). VSE misclassified a lower percentage of polyps >5mm as ≤5mm (9.4%) compared to forceps (15.7%) and ER (20.9%). VSE misclassified a lower percentage of ≥20mm polyps as <20mm (8.3%) compared with forceps (66.7%) and ER (75.0%). 25.6%, 25.5%, and 22.5% of polyps ≥10mm were misclassified as <10mm with ER, forceps, and VSE, respectively. Conclusion(s) VSE had significantly higher relative accuracy in measuring polyps compared to ER or forceps assisted measurement. VSE improves correct classification of polyps at clinically important size thresholds. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest R. Djinbachian: None Declared, M. Taghiakbari: None Declared, C. Haumesser: None Declared, M. Zarandi-Nowroozi: None Declared, M. Abou-Khalil: None Declared, S. Sidani: None Declared, J. Liu: None Declared, B. Panzini: None Declared, D. von Renteln Grant / Research support from: Daniel von Renteln is supported by a “Fonds de Recherche du Québec Santé” (FRQS) career development award and has received research funding from ERBE, Ventage, Pendopharm, Fujifilm, and Pentax., Consultant of: Boston Scientific and Pendopharm,
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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| 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".