Validation of the GPAT – the Global Polypectomy Assessment Tool: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement
Bibliographic record
Abstract
Abstract Background Colorectal polypectomy is operator dependent, with variable rates of complete resection. The currently available assessment tools do not provide specific competency-based evaluation of provider technique. We aimed to validate the Global Polypectomy Assessment Tool (GPAT), a novel competency assessment tool for colorectal polypectomy. Methods GPAT was derived from the ESGE Curriculum for Training in endoscopic mucosal resection in the colon. Members of the curriculum taskforce plus three invited trainees and three medical students (collectively: the assessors) anonymously assessed nine endoscopic-view only polypectomy videos. The primary end point was the correlation of the assessors’ GPAT scores with a consensus-derived reference GPAT score per video. Secondary end points were the assessors’ subjective impression versus their GPAT score and interobserver agreement among assessors’ GPAT scores. Results 171 GPAT assessments by 19 assessors (consultant gastroenterologists [n = 10], trainee gastroenterologists [n = 4], consultant surgeons [n = 2], and medical students [n = 3]) were analyzed. Reference GPAT scores did not differ significantly from those of the assessors (73.1 % [95 %CI 64.6 %–81.6 %] vs. 69.3 % [95 %CI 64.9 %–81.2 %]; P = 0.47). There was moderate IOA in GPAT scores among gastroenterologists (intraclass correlation coefficient [ICC], 0.52 [moderate]) but not among nongastroenterologists (ICC 0.32 [poor]). GPAT correlated with assessors’ subjective impression of polypectomy quality (correlation coefficient 0.98 [95 %CI 0.90–1.00]; P < 0.001). Overall assessors’ qualitative usability scoring of GPAT was positive. Conclusions GPAT allows standardized scoring of polypectomies, with moderate IOA among gastroenterologists and correlation with subjective impressions of polypectomy quality. GPAT could standardize assessment of trainee polypectomy competency offering structured feedback on performance.
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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 | 0.032 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".