Establishing reference standards for small and diminutive colorectal polyp size: validation of caliper-based size measurement
Bibliographic record
Abstract
Background and Aim: Accurate measurement of resected colorectal polyps is essential for clinical management, research, and the development of artificial intelligence-based size estimation systems. Despite widespread use of caliper-based measurement for specimen sizing, formal validation against a reference standard is lacking. This study aimed to validate caliper-based measurement of resected small and diminutive colorectal polyps against high-resolution digital microscopy, a previously validated reference method. Methods: At the Centre hospitalier de l'Université de Montréal, 143 polyps from 92 patients were measured immediately after resection using vernier digital calipers in the endoscopy suite. Independent measurements were subsequently obtained using high-resolution digital microscopy under blinded conditions. Agreement between methods was assessed using bias analysis, Bland-Altman limits of agreement, intraclass correlation coefficient (ICC), and categorical size concordance. Results: < .001) relative to the reference standard. The noninferiority hypothesis with a 0.5-mm margin was not rejected (lower 95% CI > -0.5 mm). Bland-Altman's limits of agreement were -1.57 to 1.12 mm, and the ICC was 0.88 (95% CI: 0.82-0.92). Correct categorical classification occurred in 94.4% of cases (95% CI: 0.89-0.97; κ = 0.81). Conclusion: Caliper-based measurement provides accurate and reproducible estimates of polyp size when compared with digital microscopy, supporting its use for clinical and research applications requiring direct specimen measurement.
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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 | 0.042 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".