Comparing size measurements of simulated colorectal polyp size and morphology groups when using a virtual scale endoscope or visual size estimation: Blinded randomized controlled trial
Bibliographic record
Abstract
OBJECTIVES: The virtual scale endoscope (VSE) allows projection of a virtual scale onto colorectal polyps allowing real-time size measurements. We studied the relative accuracy of VSE compared to visual assessment (VA) for the measuring simulated polyps of different size and morphology groups. METHODS: We conducted a blinded randomized controlled trial using simulated polyps within a colon model. Sixty simulated polyps were evenly distributed across four size groups (1-5, >5-9.9, 10-19.9, and ≥20 mm) and three Paris morphology groups (flat, sessile, and pedunculated). Six endoscopists performed polyp size measurements using random allocation of either VA or VSE. RESULTS: A total of 359 measurements were completed. The relative accuracy of VSE was significantly higher when compared to VA for all size groups >5 mm (P = 0.004, P < 0.001, P < 0.001). For polyps ≤5 mm, the relative accuracy of VSE compared to VA was not significantly higher (P = 0.186). The relative accuracy of VSE was significantly higher when compared to VA for all morphology groups. 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 VA (11.2%, 24.7%, and 52.3% respectively; P = 0.008, P < 0.001, and P = 0.003). CONCLUSION: Virtual scale endoscope had significantly higher relative accuracies for every polyp size group or morphology type aside from diminutive. VSE enables the endoscopist to better classify polyps into correct size categories at clinically relevant size thresholds of 5, 10, and 20 mm.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 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 | 0.005 | 0.002 |
| 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".