A systematic review of facial plastic surgery simulation training models
Bibliographic record
Abstract
OBJECTIVES: The coronavirus disease 2019 pandemic has led to a need for alternative teaching methods in facial plastics. This systematic review aimed to identify facial plastics simulation models, and assess their validity and efficacy as training tools. METHODS: Literature searches were performed. The Beckman scale was used for validity. The McGaghie Modified Translational Outcomes of Simulation-Based Mastery Learning score was used to evaluate effectiveness. RESULTS: Overall, 29 studies were selected. These simulated local skin flaps (n = 9), microtia frameworks (n = 5), pinnaplasty (n = 1), facial nerve anastomosis (n = 1), oculoplastic procedures (n = 5), and endoscopic septoplasty and septorhinoplasty simulators (n = 10). Of these models, 14 were deemed to be high-fidelity, 13 low-fidelity and 2 mixed-fidelity. None of the studies published common outcome measures. CONCLUSION: Simulators in facial plastic surgical training are important. These models may have some training benefits, but most could benefit from further assessment of validity.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".