Application of stereoscopic visualization on surgical skill acquisition in novices
Bibliographic record
Abstract
This study examines the influence of monoscopic vs. stereoscopic visualization in novice trainees performing the MISTELS, a validated laparoscopic skill evaluation system consisting of 5 distinct tasks. We hypothesize a difference in performance based on visualization modality. First and second year medical students (n=31) performed the MISTELS battery of tasks using either monoscopic or stereoscopic visualization displays. Regression analysis indicates performance was not correlated to participant manual dexterity or visual spatial ability (p>;0.05). Monoscopic visualization was shown to produce significantly better performance in the peg transfer task alone (p=0.001), with visualization modality producing no significant difference in performance of the remaining tasks (p>;0.05). Qualitatively, 57.1% of participants believed their performance was aided by stereoscopic visualization. Most participants rated the peg transfer task the least difficult task (60%), and the intracorporeal knot‐tying task the most difficult (65.9%). These results suggest the intrinsic difficulty of the MISTELS tasks may exceed a novice user's skill, rendering no benefit with additional 3D cues in naïve surgical trainees, and may serve to increase cognitive load, potentially decrease skill acquisition and learning. Grant Funding Source : Summer Research Training Program, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Canada
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".