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Virtual reality simulation training for health professions trainees in gastrointestinal endoscopy

2018· review· en· W3164525568 on OpenAlexaff
Rishad Khan, Joanne Plahouras, Bradley C. Johnston, Michael A. Scaffidi, Samir C. Grover, Catharine M. Walsh

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2018
Typereview
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsHospital for Sick ChildrenWestern UniversitySt. Michael's HospitalUniversity of TorontoDalhousie UniversityCanada Research Chairs
Fundersnot available
KeywordsCINAHLMedicineEndoscopyVirtual realityMEDLINESigmoidoscopyMedical educationApprenticeshipSimulation trainingMedical physicsCochrane LibraryColonoscopyRandomized controlled trialComputer scienceNursingSimulationSurgeryInternal medicineArtificial intelligence

Abstract

fetched live from OpenAlex

Background Endoscopy has traditionally been taught with novices practicing on real patients under the supervision of experienced endoscopists. Recently, the growing awareness of the need for patient safety has brought simulation training to the forefront. Simulation training can provide trainees with the chance to practice their skills in a learner‐centred, risk‐free environment. It is important to ensure that skills gained through simulation positively transfer to the clinical environment. This updated review was performed to evaluate the effectiveness of virtual reality (VR) simulation training in gastrointestinal endoscopy. Objectives To determine whether virtual reality simulation training can supplement and/or replace early conventional endoscopy training (apprenticeship model) in diagnostic oesophagogastroduodenoscopy, colonoscopy, and/or sigmoidoscopy for health professions trainees with limited or no prior endoscopic experience. Search methods We searched the following health professions, educational, and computer databases until 12 July 2017: the Cochrane Central Register of Controlled Trials, Ovid MEDLINE, Ovid Embase, Scopus, Web of Science, BIOSIS Previews, CINAHL, AMED, ERIC, Education Full Text, CBCA Education, ACM Digital Library, IEEE Xplore, Abstracts in New Technology and Engineering, Computer and Information Systems Abstracts, and ProQuest Dissertations and Theses Global. We also searched the grey literature until November 2017. Selection criteria We included randomised and quasi‐randomised clinical trials comparing VR endoscopy simulation training versus any other method of endoscopy training with outcomes measured on humans in the clinical setting, including conventional patient‐based training, training using another form of endoscopy simulation, or no training. We also included trials comparing two different methods of VR training. Data collection and analysis Two review authors independently assessed the eligibility and methodological quality of trials, and extracted data on the trial characteristics and outcomes. We pooled data for meta‐analysis where participant groups were similar, studies assessed the same intervention and comparator, and had similar definitions of outcome measures. We calculated risk ratio for dichotomous outcomes with 95% confidence intervals (CI). We calculated mean difference (MD) and standardised mean difference (SMD) with 95% CI for continuous outcomes when studies reported the same or different outcome measures, respectively. We used GRADE to rate the quality of the evidence. Main results We included 18 trials (421 participants; 3817 endoscopic procedures). We judged three trials as at low risk of bias. Ten trials compared VR training with no training, five trials with conventional endoscopy training, one trial with another form of endoscopy simulation training, and two trials compared two different methods of VR training. Due to substantial clinical and methodological heterogeneity across our four comparisons, we did not perform a meta‐analysis for several outcomes. We rated the quality of evidence as moderate, low, or very low due to risk of bias, imprecision, and heterogeneity. Virtual reality endoscopy simulation training versus no training: There was insufficient evidence to determine the effect on composite score of competency (MD 3.10, 95% CI ‐0.16 to 6.36; 1 trial, 24 procedures; low‐quality evidence). Composite score of competency was based on 5‐point Likert scales assessing seven domains: atraumatic technique, colonoscope advancement, use of instrument controls, flow of procedure, use of assistants, knowledge of specific procedure, and overall performance. Scoring range was from 7 to 35, a higher score representing a higher level of competence. Virtual reality training compared to no training likely provides participants with some benefit, as measured by independent procedure completion (RR 1.62, 95% CI 1.15 to 2.26; 6 trials, 815 procedures; moderate‐quality evidence). We evaluated overall rating of performance (MD 0.45, 95% CI 0.15 to 0.75; 1 trial, 18 procedures), visualisation of mucosa (MD 0.60, 95% CI 0.20 to 1.00; 1 trial, 55 procedures), performance time (MD ‐0.20 minutes, 95% CI ‐0.71 to 0.30; 2 trials, 29 procedures), and patient discomfort (SMD ‐0.16, 95% CI ‐0.68 to 0.35; 2 trials, 145 procedures), all with very low‐quality evidence. No trials reported procedure‐related complications or critical flaws (e.g. bleeding, luminal perforation) (3 trials, 550 procedures; moderate‐quality evidence). Virtual reality endoscopy simulation training versus conventional patient‐based training: One trial reported composite score of competency but did not provide sufficient data for quantitative analysis. Virtual reality training compared to conventional patient‐based training resulted in fewer independent procedure completions (RR 0.45, 95% CI 0.27 to 0.74; 2 trials, 174 procedures; low‐quality evidence). We evaluated performance time (SMD 0.12, 95% CI ‐0.55 to 0.80; 2 trials, 34 procedures), overall rating of performance (MD ‐0.90, 95% CI ‐4.40 to 2.60; 1 trial, 16 procedures), and visualisation of mucosa (MD 0.0, 95% CI ‐6.02 to 6.02; 1 trial, 18 procedures), all with very low‐quality evidence. Virtual reality training in combination with conventional training appears to be advantageous over VR training alone. No trials reported any procedure‐related complications or critical flaws (3 trials, 72 procedures; very low‐quality evidence). Virtual reality endoscopy simulation training versus another form of endoscopy simulation: Based on one study, there were no differences between groups with respect to composite score of competency, performance time, and visualisation of mucosa. Virtual reality training in combination with another form of endoscopy simulation training did not appear to confer any benefit compared to VR training alone. Two methods of virtual reality training: Based on one study, a structured VR simulation‐based training curriculum compared to self regulated learning on a VR simulator appears to provide benefit with respect to a composite score evaluating competency. Based on another study, a progressive‐learning curriculum that sequentially increases task difficulty provides benefit with respect to a composite score of competency over the structured VR training curriculum. Authors' conclusions VR simulation‐based training can be used to supplement early conventional endoscopy training for health professions trainees with limited or no prior endoscopic experience. However, we found insufficient evidence to advise for or against the use of VR simulation‐based training as a replacement for early conventional endoscopy training. The quality of the current evidence was low due to inadequate randomisation, allocation concealment, and/or blinding of outcome assessment in several trials. Further trials are needed that are at low risk of bias, utilise outcome measures with strong evidence of validity and reliability, and examine the optimal nature and duration of training.

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)0.006
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0110.000

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 head0.362
GPT teacher head0.502
Teacher spread0.140 · 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 designSystematic review
Domainnot available
GenreReview

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".

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Citations143
Published2018
Admission routes1
Has abstractyes

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