Nonbiological Microsurgery Simulators in Plastic Surgery Training: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Simulation has gained notable recognition for its role as an effective training and assessment modality in the present era of competency-based medical education. Despite the well-documented efficacy of both live and cadaveric animal models, several ethical, financial, and accessibility issues persist with their use. Lower fidelity nonbiological simulators have gained recognition for their ability to circumvent these challenges. This systematic review reports on all prosthetic and virtual reality simulators in use for microsurgery training, with an emphasis on each model's complexity, characteristics, advantages, disadvantages, and validation measures taken. METHODS: A systematic search was performed using the National Library of Medicine (PubMed), MEDLINE, and Embase databases. Search terms were those pertaining to prosthetic and virtual reality models with relevance to microsurgical training in plastic surgery. Three independent reviewers evaluated all articles retrieved based on strict inclusion and exclusion criteria. RESULTS: Fifty-seven articles met the inclusion criteria for review, reporting on 20 basic prosthetic models, 20 intermediate models, 13 advanced models, and six virtual reality simulators. CONCLUSIONS: A comprehensive summary has been compiled of all nonbiological simulators in use for microsurgery training in plastic surgery, demonstrating efficacy for the acquisition and retention of microsurgical skills. Metrics-based validation efforts, however, were often lacking in the literature. As plastic surgery programs continue to innovate, ensure accountability, and safely meet today's training standards, prosthetic simulators are set to play a larger role in the development of a standardized, ethical, accessible, and objectively measurable microsurgery training curriculum for the modern-day plastic and reconstructive surgery resident.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".