SMaRT Assessment Tool: An Innovative Approach for Objective Assessment of Flap Designs
Bibliographic record
Abstract
SUMMARY: The teaching and assessment of ideal surgical markings for local flaps required for optimal aesthetic and functional outcomes remain a challenge in the present era of competency-based surgical education. The authors utilized the bilobed flap for nasal reconstruction as a proof of concept for the development of an innovative objective assessment tool based on statistical shape analysis, with a focus on providing automated, evidence-based, objective, specific, and practical feedback to the learner. The proposed tool is based on Procrustes statistical shape analysis, previously used for the assessment of facial asymmetry in plastic surgery. For performance boundary testing, a series of optimal and suboptimal designs generated in deliberate violation of the established ideals of optimal bilobed flap design were evaluated, and a four-component feedback score of Scale, Mismatch, Rotation, and Translation (SMaRT) was generated. The SMaRT assessment tool demonstrated the capacity to proportionally score a spectrum of designs (n = 36) ranging from subtle to significant variations of optimal, with excellent computational and clinically reasonable performance boundaries. In terms of shape mismatch, changes in SMaRT score also correlated with intended violations in designs away from the ideal flap design. This innovative educational approach could aid in incorporating objective feedback in simulation-based platforms in order to facilitate deliberate practice in flap design, with the potential for adoption in other fields of plastic surgery to automate assessment processes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".