Recent evidence on visual-spatial ability in surgical education: A scoping review
Bibliographic record
Abstract
BACKGROUND: Understanding the relationships between structures is critical for surgical trainees. However, the heterogeneity of the literature on visual-spatial ability (VSA) in surgery makes it challenging for educators to make informed decisions on incorporating VSA into their programs. We conducted a scoping review of the literature on VSA in surgery to provide a map of the literature and identify where gaps still exist for future research. METHODS: We searched databases until December 2019 using keywords related to VSA and surgery. The resulting articles were independently screened by two researchers for inclusion in our review. RESULTS: We included 117 articles in the final review. Fifty-nine articles reported significant correlations between VSA tests and surgical performance, and this association is supported by neuroimaging studies. However, it remains unclear whether VSA should be incorporated into trainee selection and whether there is a benefit of three-dimensional (3D) over two-dimensional (2D) training. CONCLUSIONS: It appears that VSA correlates with surgical performance in the simulated environment, particularly for novice learners. Based on our findings, we make suggestions for how surgical educators may use VSA to support novice learners. Further research should determine whether VSA remains correlated to surgical performance when trainees move into the operative environment.
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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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".