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Record W2792746965 · doi:10.1093/jcag/gwy009.204

A204 IMPACT OF A SIMULATION-BASED TRAINING CURRICULUM USING GAMIFICATION FOR COLONOSCOPY: A RANDOMIZED CONTROLLED TRIAL

2018· article· en· W2792746965 on OpenAlexaffabout
Michael A. Scaffidi, Catharine M. Walsh, M Pearl, Rishad Khan, Ruben Kalaichandran, Eric Lui, Kathleen Winger, Ahmed Al‐Mazroui, M J Abunassar, Samir C. Grover

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2018
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsSickKids FoundationThe Wilson CentreHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsCurriculumColonoscopyRandomized controlled trialMedicinePhysical therapyCognitive loadCognitionClass (philosophy)Significant differenceMedical educationPsychologySurgeryInternal medicineComputer sciencePedagogyArtificial intelligence

Abstract

fetched live from OpenAlex

Simulation-based training (SBT) curricula for training novices in gastrointestinal (GI) endoscopy have been shown to be effective. Gamification, the application of principles from games such as competition and rewards, has been used to enhance procedural learning in healthcare. There are no known applications of gamification for training of GI endoscopy, however. To evaluate a SBT curriculum using gamification for novice endoscopists on the performance of simulated colonoscopies, compared to a conventional SBT curriculum. Twenty-one novice endoscopists (completed <20 previous colonoscopies) from the general surgery and gastroenterology programs at the University of Toronto participated. Participants were randomized into the Conventional Training Curriculum (CTC) or the Gamified Integrated Curriculum (GIC) Group. Both groups received the same SBT curriculum on two simulator models, a benchtop and EndoVR® model. The GIC included a game-board, game narrative, badges for training landmarks, and rewards for top performance. Performance was assessed at three points: prior to training, immediately after training, and 4 to 6 weeks after training. Assessments took place on the EndoVR® simulator. The primary outcome measure was the difference in colonoscopic performance between the two groups, assessed using the Joint Advisory Group for GI Endoscopy Direct Observation of Procedural Skills (JAG DOPS). The secondary outcome was the difference in cognitive load, as rated by participants themselves using the Cognitive Load Index for Colonoscopy (CLIC), which measures intrinsic, extrinsic, and germane cognitive load. For the JAG DOPS scores, there was no significant difference between the two groups with respect to performance of simulated colonoscopies (P>0.05). For the CLIC, there was a significant difference between the two groups immediately after training for intrinsic load, as the GIC demonstrated a significantly lower load (P=0.04). There were no other significant differences between the two groups for cognitive load. We found that a SBT curriculum using gamification for colonoscopy was associated with a significantly lower intrinsic load after training. A lower instrinsic load indicates that participants in the GIC group found that the task of colonoscopy was not as difficult as their counterparts in the CTC group. Although we did not find a difference with respect to colonoscopic performance, these findings represent an interim analysis. The completion of this study will likely yield important insight into further improvements of endoscopic training. 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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.030
GPT teacher head0.337
Teacher spread0.307 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes2
Has abstractyes

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