A Preclinical Blinded Randomized-Controlled Trial Evaluating the Clinical Relevance of Polyp Size Measurement Using a Virtual Scale Endoscope
Bibliographic record
Abstract
Abstract Background The virtual scale endoscope (VSE) helps endoscopists measure colorectal polyp size more accurately compared to visual assessment (VA). However, previous studies were not adequately powered to evaluate the sizing of polyps at clinically relevant size thresholds and relative accuracy for size subgroups. Methods We created 64 artificial polyps of varied sizes and Paris class morphology, randomly assigned 1:1 to be measured (383 total measurement datapoints with VSE and VA by 6 endoscopists blinded to true size) in a colon model. We added data from two previous trials (480 measurement datapoints). We evaluated for correct classification of polyps into size groups at 3 mm, 5 mm, 10 mm, and 20 mm size thresholds and the relative size measurement accuracy for diminutive polyps (≤5 mm), small polyps (5–9 mm), large polyps at 10–19 mm, and polyps (≥20). Results VSE had significantly less size group misclassifications at the 5 mm, and 10 mm thresholds (28 percent vs. 45 percent, P = 0.0159 and 26 percent vs. 44 percent, P = 0.0135, respectively). For the 3 mm and 20 mm thresholds, VSE had lower misclassifications; however, this was not statistically significant (36 percent vs. 46 percent, P = 0.3853 and 38 percent vs. 41 percent, P = 0.2705, respectively). The relative size measurement accuracy was significantly higher for VSE compared to VA for all size subgroups (diminutive (P < 0.01), small polyps (P < 0.01), 10–19 mm (P < 0.01), and ≥20 mm (P < 0.01)). Conclusion VSE outperforms VA in categorizing polyps into size groups at the clinically relevant size thresholds of 5 mm and 10 mm. Using VSE resulted in significantly higher relative measurement accuracy for all size subgroups.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".