Findings Favor Haptics Feedback in Virtual Simulation Surgical Education: An Updated Systematic and Scoping Review
Bibliographic record
Abstract
BACKGROUND: Virtual simulations (VSs) enhance clinical competencies and skills. However, a previous systematic review of 9 RCT studies highlighted a paucity of literature on the effects of haptic feedback in surgical VSs. An updated systematic and scoping review was conducted to encompass more studies and a broader range of study methodologies. METHODS: A systematic literature search was conducted on July 31, 2023, in MEDLINE, Embase, and Cochrane. English language studies comparing haptic vs non-haptic conditions and using VSs were included. Studies were evaluated and reported using PRISMA-ScR guidelines. RESULTS: Out of 2782 initial studies, 51 were included in the review. Most studies used RCT (21) or crossover (23) methodologies with medical residents, students, and attending physicians. Most used post-intervention metrics, while some used pre- and post-intervention metrics. Overall, 34 performance results from studies favored haptics, 3 favored non-haptics, and the rest showed mixed or equal results. CONCLUSION: This updated review highlights the diverse application of haptic technology in surgical VSs. Haptics generally enhances performance, complements traditional teaching methods, and offers personalized learning with adequate simulator validation. However, a sparsity of orienting to the simulator, pre-/post-study designs, and small sample sizes poses concerns with the validity of the results. We underscore the urgent need for standardized protocols, large-scale studies, and nuanced understanding of haptic feedback integration. We also accentuate the significance of simulator validation, personalized learning potential, and the need for researcher, educator, and manufacturer collaboration. This review is a guidepost for navigating the complexities and advancements in haptic-enhanced surgical VSs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".