A102 NOVICE ENDOSCOPISTS EXPERIENCE AND PERFORMANCE ON A NOVEL PHYSICAL-COMPUTER COLONOSCOPY SIMULATOR
Bibliographic record
Abstract
Abstract Background Simulation-based training mitigates safety risks for patients in the early stages of endoscopic training. The MIKOTO colonoscopy simulator is a hybrid physical-computer model that assesses forces applied during simulated procedures via sensor-generated feedback to the user. Aims To describe the technical performance and self-reported experience of novice endoscopists when using the MIKOTO. Methods 24 novices attended a simulation course in Toronto which included activities on benchtop, colon phantoms, and the MIKOTO. Trainees completed MIKOTO’s easiest case in 10 minutes. The primary outcome measure was the self-reported user experience using a 5-point Likert scale. Other outcomes included relevant DOPS items rated by an expert, and a MIKOTO-generated score integrating colon elongation and completion time. Results 11 Gastroenterology and 13 General Surgery novices (<500 procedures) attended. The median rating for “feels like the low-fidelity (wooden simulator)” was 2 with an IQR of 2 (Table 1). The median rating for “looks like performing colonoscopy on a live patient” was 4 with an IQR of 2. The median score for “Further use of the Mikoto will help improve my overall colonoscopy performance” was 5. The median ratings on relevant DOPS items ranged from 1 to 2. The median MIKOTO score was 27.5 with an interquartile range (IQR) of 5. Conclusions The expert-assessed DOPS rating and Mikoto-generated scores indicate the ability of the Mikoto to show the skill level of the novice trainees. Self-reported experiences indicate similarity of visual and haptic realism compared to live procedures. The MIKOTO, however, was distinct from the low-fidelity benchtop simulator. A limitation of this study is the brief time spent on the simulator and the lack of orientation to the MIKOTO. Further studies are needed to compare technical performance and subjective experience between novices, intermediate and expert endoscopists. Different forms of validity must be investigated. The experience of the novice endoscopists provide insight on future integration of the MIKOTO into training programs. Table 1. Mikoto Experience Form Demographics and DOPS Ratings 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".