Teaching and Assessing Cognitive Competencies in Aesthetic and Plastic Surgery
Bibliographic record
Abstract
BACKGROUND: Cognition, part of surgical competence, employs forward planning, error prevention, and orchestration of technical skills. Thus, an understanding of mental processes used by experts throughout patient care is essential to teaching such competencies. The authors' study aimed to analyze and compare mental models of two distinct procedures in plastic surgery-breast augmentation and flexor tendon repair-to develop a framework to define cognitive competencies in plastic surgery aided by a review of the literature. METHODS: Based on data from a priori cognitive task analyses, literary sources, and field observations of breast augmentation surgery and flexor tendon repair, task lists were produced for each procedure. Two mental models were developed using fuzzy logic cognitive maps to visually illustrate and analyze cognitive processes used in either procedure. A comparison of the models aided by literature was used to define the cognitive competencies employed, identify differences in the decision-making process, and provide a guiding framework for understanding cognitive competencies. RESULTS: Five distinct cognitive competency domains were identified from the literature applicable to plastic surgery: situation awareness, decision-making, task management, leadership, and communication and teamwork. Differences in processes of decision-making utilized between an elective and a trauma setting were identified. A framework to map cognitive competencies within a generic mental model in surgical care was synthesized, and methods were suggested for training on such competencies. CONCLUSION: Cognitive competencies in different settings in plastic surgery are introduced using a comparative study of two mental models of distinct procedures to guide the teaching and assessment of such competencies.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".