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Peer-reviewedCC BY-NC-NDPlastic and reconstructive surgery. Global open

Microsurgical Training and Skill Acquisition: A Systematic Review and Meta-analysis.

  1. 1From the Department of Plastic and Reconstructive Surgery, University of the Free State, Bloemfontein, South Africa.
  2. 2Department of Plastic Surgery, Faculty of Health Sciences, University of the Free State, South Africa.
  3. 3Private Practice, St Martha's Regional Hospital Nova Scotia, Antigonish, Nova Scotia, Canada.

Abstract

Background

Simulation in the context of microsurgical training has been explored and examined in the literature with a difference of opinion regarding the optimal model to use for the training and acquisition of microsurgical skills. In some reports, the fidelity of the live animal models offers the opportunity learn surgical as well as surgical-scenario contextualized skills that a less realistic model is unable to offer. In other publications, the skills that can be taught on lower fidelity models are equivalent and offer a more viable and feasible approach to surgical training.Methods

A systematic review of the available microsurgical simulation models was performed with a focus on the acquisition of microsurgical skills measured with validated microsurgical instruments. A meta-analysis of the subgroups was then performed to identify the optimal microsurgical training tool.Results

All training with simulation models resulted in large and statistically significant improvements in microsurgical skills across the range of groupings of microsurgical simulations. This effect was consistently identified across the different groups.Conclusions

Choosing a model to train microsurgical skills may be less important than initially thought. Across the range of living and nonliving biological models and synthetic models, the potential to improve microsurgical skills significantly is available regardless of which simulation model is chosen.

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BibTeX
@article{elliott2026microsurgical,
  title = {{Microsurgical Training and Skill Acquisition: A Systematic Review and Meta-analysis.}},
  author = {Abigail E Elliott and Travis M Page and Corné P G Nel},
  journal = {Plastic and reconstructive surgery. Global open},
  year = {2026},
  month = oct,
  doi = {10.1097/gox.0000000000008149},
  url = {https://europepmc.org/article/MED/42903278},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Microsurgical Training and Skill Acquisition: A Systematic Review and Meta-analysis.
AU  - Abigail E Elliott
AU  - Travis M Page
AU  - Corné P G Nel
PY  - 2026
DA  - 2026/10/20
JO  - Plastic and reconstructive surgery. Global open
DO  - 10.1097/gox.0000000000008149
UR  - https://europepmc.org/article/MED/42903278
AB  - Background  Simulation in the context of microsurgical training has been explored and examined in the literature with a difference of opinion regarding the optimal model to use for the training and acquisition of microsurgical skills. In some reports, the fidelity of the live animal models offers the opportunity learn surgical as well as surgical-scenario contextualized skills that a less realistic model is unable to offer. In other publications, the skills that can be taught on lower fidelity models are equivalent and offer a more viable and feasible approach to surgical training.Methods  A systematic review of the available microsurgical simulation models was performed with a focus on the acquisition of microsurgical skills measured with validated microsurgical instruments. A meta-analysis of the subgroups was then performed to identify the optimal microsurgical training tool.Results  All training with simulation models resulted in large and statistically significant improvements in microsurgical skills across the range of groupings of microsurgical simulations. This effect was consistently identified across the different groups.Conclusions  Choosing a model to train microsurgical skills may be less important than initially thought. Across the range of living and nonliving biological models and synthetic models, the potential to improve microsurgical skills significantly is available regardless of which simulation model is chosen.
SN  - 2169-7574
ER  -