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Virtual reality simulation training for health professions trainees in gastrointestinal endoscopy

2025· article· en· W4414065513 on OpenAlexaff
Nasruddin Sabrie, Rishad Khan, Joanne Plahouras, Bradley C. Johnston, Michael A. Scaffidi, Samir C. Grover, Catharine M. Walsh

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2025
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsThe Wilson CentreSickKids FoundationUniversity of TorontoUniversity Health NetworkThe Scarborough HospitalUniversity of OttawaUniversity of Calgary
Fundersnot available
KeywordsVirtual realityEndoscopyHealth professionsSimulation trainingTraining (meteorology)MEDLINE

Abstract

fetched live from OpenAlex

BACKGROUND: Training in endoscopy has traditionally been based upon an apprenticeship model, where novices develop their skills on real patients under the supervision of experienced endoscopists. In an effort to prioritise patient safety, simulation training has emerged as a means to allow novices to practice in a risk-free environment. This is the second update of the review, which was first published in 2012 and updated in 2018. It evaluates the effectiveness of virtual reality (VR) simulation training in gastrointestinal endoscopy. OBJECTIVES: To determine whether VR 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 CENTRAL, MEDLINE, Embase, and 13 other databases, together with reference checking and handsearching of review articles, conference abstracts and proceedings, to identify the studies included in the review. We conducted database searches to 18 October 2023, and grey literature searches to December 2023. SELECTION CRITERIA: We included randomised and quasi-randomised clinical trials comparing VR endoscopy simulation training to any other method of endoscopy training (e.g. conventional patient-based training, another form of endoscopy simulation), or no training. We also included trials comparing two different methods of VR training. We included only trials evaluating outcomes on humans in the clinical setting. Participants were health professions trainees: physicians (medical students, residents, fellows, and practitioners), nurses, and physician assistants with limited or no prior endoscopy experience. DATA COLLECTION AND ANALYSIS: Two authors independently assessed the eligibility and methodological quality of trials, and extracted trial characteristics and outcome data. The primary outcome was the composite score of competency, as defined by authors. Secondary outcomes were independent procedure completion, performance time, adverse event or critical flaw occurrence, patient discomfort, global rating of performance, and visualisation of mucosa. 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 ratios (RRs) for dichotomous outcomes with 95% confidence intervals (CIs). We calculated mean differences (MDs) and standardised mean differences (SMDs) with 95% CIs for continuous outcomes when studies reported the same or different outcome measures, respectively. We used GRADE to rate the certainty of evidence. We assessed the risk of bias using the original Cochrane domain-based tool. MAIN RESULTS: We included 20 trials (500 participants; 3975 endoscopic procedures). We judged four trials (20%) 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 four trials compared 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 certainty of evidence as moderate, low, or very low due to risk of bias, imprecision, and heterogeneity. VR endoscopy simulation training versus no training. The 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. The scoring range was from 7 to 35; higher scores mean greater competence. Compared to no training, VR training may result in little to no difference in composite score of competency (MD 3.10, 95% CI -0.16 to 6.36; 1 trial, 24 procedures; low-certainty evidence). VR training likely provides participants with a benefit, as measured by independent procedure completion (RR 1.62, 95% CI 1.15 to 2.26; 6 trials, 815 procedures; moderate-certainty evidence). The evidence is very uncertain about the effects of VR simulation on 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). The three trials which reported on procedure-related adverse events or critical flaws reported no incidences in either group (550 procedures; moderate-certainty evidence). VR endoscopy simulation training versus conventional patient-based training. One trial reported a composite score of competency but did not provide sufficient data for quantitative analysis. VR training compared to conventional patient-based training may result in fewer independent procedure completions (RR 0.45, 95% CI 0.27 to 0.74; 2 trials, 174 procedures; low-certainty evidence). The evidence is very uncertain about the effects of VR simulation on 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). VR training in combination with conventional training appears to be advantageous over VR training alone. The three trials which reported on procedure-related adverse events or critical flaws reported no incidences in either group (72 procedures; very low-certainty evidence). AUTHORS' CONCLUSIONS: Despite moderate- to very low-certainty evidence, we can conclude that VR training, as compared with no training, generally appears to provide participants with some advantage over their untrained peers, as measured by independent procedure completion, overall rating of performance or competency, and mucosal visualisation. We found insufficient evidence to advise for or against the use of VR simulation-based training as a replacement for early conventional endoscopy training. Further research is needed to help establish the potential use of VR simulation-based training to supplement and/or replace conventional endoscopy 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)0.007
metaresearch head score (Gemma)0.030
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 score0.009
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.205
GPT teacher head0.464
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

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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Citations2
Published2025
Admission routes1
Has abstractyes

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