The Montreal Augmentation Mammaplasty Operation (MAMO) Simulator: An Alternative Method to Train and Assess Competence in Breast Augmentation Procedures
Bibliographic record
Abstract
BACKGROUND: Surgical residents' exposure to aesthetic procedures remains limited in residency training. The development of the Montreal augmentation mammaplasty operation (MAMO) simulator aims to provide an adjunctive training method and assessment tool to complement the evolving competency-based surgical curriculum. OBJECTIVES: To perform face, content, and construct validations of the MAMO simulator for subpectoral breast augmentation procedures and assess the reliability of the assessment scales used. METHODS: Plastic surgery staff and residents were recruited to perform a subpectoral breast augmentation on the simulator. Video recordings of their performance were blindly evaluated using the objective structured assessment of technical skills (OSATS) system consisting of the global rating scale (GRS), mammaplasty objective assessment tool (MOAT), and a surgery-specific Checklist score. RESULTS: Fourteen plastic surgery residents and seven expert plastic surgeons were enrolled. Experts' performance was significantly higher than residents' according to each of GRS, MOAT, and Checklist scores. Mean values of residents and experts were 23.4 (2.5) vs 36.9 (3.1) (P < 0.0001) for GRS score, 30.4 (2.2) vs 40 (3.2) (P < 0.0001) for MOAT scores, and 9.7 (1.5) vs 12 (1) (P < 0.001) for Checklist scores, respectively. Face and content validations showed excellent results among parameters evaluated, with an overall mean score of 4.8 (0.3) on 5. Cronbach's alpha was 0.96 and 0.83 for GRS and MOAT scores, respectively. Intraclass correlation coefficients for interrater reliability were excellent at 0.93, 0.92, and 0.89 for the GRS, MOAT, and Checklist scores, respectively. CONCLUSIONS: This study proves the construct simulator to be valid and the assessment scales to be reliable.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".