A Shift on the Horizon: A Systematic Review of Assessment Tools for Plastic Surgery Trainees
Bibliographic record
Abstract
BACKGROUND: As plastic surgery programs transition toward competency-based medical education curricula, it is important to critically assess current methods of evaluating trainee competence. The purpose of this systematic review was to identify and evaluate assessment tools for technical and nontechnical competencies in plastic surgery. METHODS: A systematic search using keywords related to competency-based medical education, assessment, and plastic surgery was conducted. Two independent reviewers extracted data pertaining to study characteristics, study design, and psychometric properties. Data pertaining to the establishment of competence and barriers to tool implementation were noted. RESULTS: Twenty-three studies were included in this review. Technical competencies were assessed in 16 studies. Nontechnical competencies were assessed in five studies. Two studies assessed both technical and nontechnical competence. Six tools were implemented in a simulated setting and 17 tools were implemented in a clinical setting. Thirteen studies (57 percent) did not report reliability scores and nine (39 percent) did not report validity scores. Two tools established clear definitions for competence. Common barriers to implementation included high demands on resources and time, uncertainty about simulation transferability, and assessor burnout. CONCLUSIONS: A number of tools exist to assess a range of plastic surgery skills, in both clinical and simulated settings. There is a need to determine the transferability of simulated assessments to clinical practice, as most available tools are simulation-based. Although additional psychometric testing of current assessment tools is required, particularly in the nontechnical domain, this review provides a base on which to build assessment frameworks that will support plastic surgery's transition to competency-based medical education.
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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 | 0.043 | 0.196 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".