Visual Arts-Based Interventions in Surgical Education
Bibliographic record
Abstract
OBJECTIVE: To systematically review the evidence for visual arts-based interventions (VABI) and their outcomes in surgical education. BACKGROUND: Although VABI shows promise in medical education, its evaluation in surgical training remains limited. There are gaps in understanding the integration of these interventions into surgical curricula and the outcomes to measure. This study explored the effectiveness of VABI across surgical education, particularly its impact on knowledge acquisition and skill development. METHODS: A literature search was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was performed across Web of Science, Preprint Citation Index, MEDLINE, and Embase. Two independent reviewers screened and extracted the data. Publications that explicitly utilized VABI and reported on knowledge or technical skill acquisition among surgical learners were selected. Study quality was assessed using the Medical Education Research Study Quality Instrument (MERSQI) scale. RESULTS: Twenty-four studies were included, which employed clay modeling (n=13 ), anatomy drawing (n=9 ), painting of three-dimensional (3D) structures (n=2), cross suturing (n=1), and paper manipulation (n=1). The participants included undergraduate medical learners (n=16) and postgraduate medical learners (n=12 ). The outcomes assessed included knowledge of anatomy/procedures (n=22 ) and accuracy of completing technical tasks (n=2). All the studies demonstrated improvements in both domains. The mean MERSQI score was 9.7/18. CONCLUSIONS: Despite intervention and outcome heterogeneity, the VABI demonstrate evidence of contributes to improved knowledge acquisition and technical skills in surgical education. Future research should prioritize high-quality studies to compare VABI with traditional teaching methods that explore its long-term impacts.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".