A106 COMPARING SIZE MEASUREMENTS OF SIMULATED COLORECTAL POLYPS SIZE AND MORPHOLOGY GROUPS WHEN USING A VIRTUAL SCALE ENDOSCOPE OR VISUAL SIZE ESTIMATION: A BLINDED RANDOMIZED CONTROLLED TRIAL
Bibliographic record
Abstract
Abstract Background The polypectomy technique used to remove colorectal polyps is influenced by the size of the polyp. Furthermore, the criteria for assigning surveillance intervals after polypectomy are based on size and pathology results. Visual size assessment is potentially fraught with being inaccurate. The virtual scale endoscope (VSE) allows projection of a virtual scale onto colorectal polyps allowing real-time size measurements. Purpose We studied the relative accuracy of VSE compared to visual assessment (VA) for the measuring simulated polyps of different size and morphology groups. Method We conducted a blinded randomized controlled trial using simulated polyps imbedded within a colon model. Sixty simulated polyps were created and evenly distributed across four different size groups (0-4.9 mm, 5-9.9 mm, 10-19.9 mm and ≥ 20 mm) and 3 different Paris morphology groups (flat, sessile and pedunculated). Six endoscopists (3 staff gastroenterologists and 3 trainees) performed size measurements of all sixty simulated polyps using random allocation of either VA or VSE. Result(s) A total of 359 measurements were completed. The relative accuracy of VSE was significantly higher when compared to VA for polyps ≥ 5 and <10mm, ≥ 10 and <20mm, ≥ 20mm (p=0.004; p<0.001, p<0.001). For polyps <5mm, the relative accuracy of VSE compared to VA was nominally higher (79.4% versus 74.1%) but this was not statistically significant (p = 0.186). The relative accuracy of VSE was higher when compared to visual assessment for sessile (p = 0.001), flat (p < 0.001) and pedunculated polyps (p = 0.002). VSE misclassified a lower percentage of ≥ 5 mm polyps as < 5 mm (2.9%), ≥ 10 mm polyps as < 10 mm (5.5%) and ≥ 20 mm polyps as < 20 mm (21.7%) compared to visual estimation (11.2; 24.7 and 52.3% respectively; p=0.008, p<0.001 and p=0.003). Conclusion(s) VSE had significantly higher relative accuracies for every polyp size group or morphology type aside from diminutive where VSE had a non-significantly higher relative accuracy. VSE enables endoscopist to better classify polyps into correct size categories at clinically relevant size thresholds of 5-, 10- and 20-mm. Implementing VSE as a standard measurement tool could allow improving clinical decision making for accurate surveillance interval assignment and choice of polypectomy technique. Please acknowledge all funding agencies by checking the applicable boxes below Other Please indicate your source of funding; The study was supported by a "Fonds de Recherche du Québec Santé" career development award (Daniel von Renteln) and a University of Montreal student award “PRogramme d’Excellence en Médecine pour l’Initiation En Recherche – PREMIER (Claire Haumesser). Disclosure of Interest C. Haumesser: None Declared, M. Zarandi-Nowroozi: None Declared, M. Taghiakbari: None Declared, R. Djinbachian: None Declared, M. Abou Khalil: None Declared, S. Sidani: None Declared, J. Liu: None Declared, B. Panzini: None Declared, I. Popescu Crainic: None Declared, D. von Renteln Grant / Research support from: ERBE Elektromedizin GmbH, Ventage, Pendopharm, Fuji and Pentax, Consultant of: Boston Scientific Inc., ERBE Elektromedizin GmbH, 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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| 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 | 0.004 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".