Is three‐dimensional videography the cutting edge of surgical skill acquisition?
Bibliographic record
Abstract
The process of learning new surgical technical skills is vital to the career of a surgeon. The acquisition of these new skills is influenced greatly by visual-spatial ability (VSA) and may be difficult for some learners to rapidly assimilate. In many cases, the role of VSA on the acquisition of a novel technical skill has been explored; however, none have probed the impact of a three-dimensional (3D) video learning module on the acquisition of new surgical skills. The first aim of this study is to capture spatially complex surgical translational flaps using 3D videography and incorporate the footage into a self-contained e-learning module designed in line with the principles of cognitive load theory. The second aim is to assess the efficacy of 3D video as a medium to support the acquisition of complex surgical skills in novice surgeons as evaluated using a global ratings scale. It is hypothesized that the addition of depth in 3D viewing will augment the learner's innate visual spatial abilities, thereby enhancing skill acquisition compared to two-dimensional viewing of the same procedure. Despite growing literature suggesting that 3D correlates directly to enhanced skill acquisition, this study did not differentiate significant results contributing to increased surgical performance. This topic will continue to be explored using more sensitive scales of measurement and more complex "open procedures" capitalizing on the importance of depth perception in surgical manipulation. Anat Sci Educ. © 2012 American Association of Anatomists.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".