A114 INTERRATER RELIABILITY BETWEEN ENDOSCOPISTS USING LOW-COST SIMULATED POLYPS
Bibliographic record
Abstract
Abstract Background The identification and removal of polyps are essential skills as they remove premalignant lesions found ad hoc during colonoscopy. Simulation training helps with trainee learning curves and help train novices in polyp identification and polypectomy skills acquisition. The cost of simulators, however, limits trainee access to simulation-based training. Aims To determine the interrater reliability between endoscopists in identifying the Paris Classification of low-cost simulated polyps. Methods Using the Paris classification, novel simulated polyps with various morphologies were developed. 5 endoscopists of varying experience levels (1 expert (>1000 endoscopies), 2 intermediates (500-1000 endoscopies), and 2 novices (<500 endoscopies)) completed a knowledge test. The knowledge test showed images of simulated polyps and the endoscopists identified the Paris classification (Figure 1). The primary outcome measure was the interrater reliability between endoscopists during the knowledge test. A two-way random effects intraclass correlation coefficient (ICC), with absolute agreement between the raters was used to estimate the interrater reliability. Another outcome measure was content validity which was assessed via survey completed by 3 experts and 21 novice endoscopists during a previous simulation course. Results The raters correctly identified all 1p polyps. The Average Measures ICC between all 5 raters was 0.961 (95% CI: 0.93-0.98) indicating excellent reliability. One rater correctly identified all 1s polyp and was removed from ICC analysis due to lack of variance. The Average Measures ICC for 1s polyps was 0.524 (95%CI: 0.15-0.76) indicating moderate reliability. All errors were misidentification of 1s for 2a polyps. The survey showed content validity in terms of setup, usefulness in trainee programs, realism, and perceived trainee improvement. Conclusions Excellent reliability for 1p polyps suggests its usefulness for training polypectomy. Further work is needed to differentiate 1s polyps from 2a polyps. A limitation to interrater reliability is the use of images instead of videos in the knowledge test. The content validity of the polyps suggests practicality for low-cost training to achieve trainee improvement. Further studies are needed to assess other polyp classifications and correlate endoscopist experience level with ability to identify the simulated polyps. Knowledge Test Scores of 5 Endoscopists ICC, intraclass correlation coefficient. (*)excluded from ICC analysis due to lack of variance. Figure 1. Simulated gel polyps based on Paris classification A-B) 1p, C-D) 1s. Funding Agencies None
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
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 | 0.046 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".