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Record W3007990492 · doi:10.1093/jcag/gwz047.142

A143 IMPACT OF A SIMULATION-BASED AUGMENTED REALITY CURRICULUM ON POLYPECTOMY SKILLS AMONG NOVICE ENDOSCOPISTS: A RANDOMIZED CONTROLLED TRIAL

2020· article· en· W3007990492 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2020
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsThe Wilson CentreUniversity of TorontoSickKids FoundationHospital for Sick ChildrenSt. Michael's HospitalQueen's University
Fundersnot available
KeywordsPolypectomyAugmented realityColonoscopyCurriculumMedicineRandomized controlled trialMedical physicsGeneral surgerySurgeryComputer scienceArtificial intelligenceInternal medicinePsychologyColorectal cancer

Abstract

fetched live from OpenAlex

Abstract Background Polypectomy is an essential endoscopic skill. Training in polypectomy has been identified as a major deficiency for endoscopists worldwide as polypectomy occurs ad hoc during a colonoscopy when a polyp is detected, and a lack of standardized curricula. Augmented reality (AR), which superimposes computer-generated images on a user’s view of the world, can address these gaps by standardizing encounters with polyps while completing simulated procedures and enabling polypectomy-specific teaching. Aims Evaluate the impact of a simulation-based augmented reality curriculum on polypectomy performance among novice endoscopists. Methods This study includes two cohorts of participants from 2019 to 2020. In 2019, participants were randomized into either: (1) a control curriculum, involving 6 hours of simulation-based training (SBT) supplemented by expert feedback, interlaced with 4 hours of small group teaching on the theory of colonoscopy; or (2) the augmented reality curriculum (ARC), in involving the same curriculum with integrated AR, wherein participants engaged with an AR-enhanced video demonstrating relevant therapeutic and pathologic details during polypectomy. The SBT for all participants involved a progressive curriculum starting on a bench-top model and then moving to the EndoVR® virtual reality simulator. The primary outcome was polypectomy-specific performance using the Direct Observation of Polypectomy Skills (DOPyS) tool during a simulated polypectomy after training, with a maximum score of 100. Results Demographic characteristics are summarized in Table. In 2019, 21 novice endoscopists were enrolled. Immediately after training, the mean DOPyS score among ARC group participants was 76.2 (SD=17.9) compared to 71.8 (SD=13.2) among control group participants (Figure). In this interim analysis, there was no significant difference between groups. Data analysis will be completed after 2020 participants complete the study. Conclusions Interim results show a trend towards improved polypectomy performance with no significant difference. The results of this study have the potential to impact polypectomy education among novices. Simulation-based AR interventions may allow learners to progress towards achieving competency in polypectomy in a risk-free environment prior to first patient contact. 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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.267
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it