Comparison of operative microscope and exoscope for execution of microanastomoses on an artificial model
Bibliographic record
Abstract
Introduction: While the improved ergonomics, depth of field, and freedom of movement offered by Exoscopes compared to Operative Microscopes are well established, their value in surgical education and training is often mentioned but remains poorly documented. Methods: In this study, we used a using a slightly modified version of the NOMAT score to compare the microvascular anastomoses on an artificial model made using traditional Operative Microscopes and the Orbeye 4K 3D Exoscope. Each participant performed the task 3 times. Results: The results showed that microscope users initially scored higher in several aspects, likely due to greater prior familiarity with the device. However, by the third repetition, the differences were no longer significant, demonstrating that the Exoscope is not inferior to the traditional Microscope in laboratory training. Moreover, the Exoscope group exhibited a faster learning curve for specific skills, highlighting its potential for early adoption by young surgeons. Discussion: These findings emphasize the educational promise of Exoscopes, particularly in facilitating a smooth transition from traditional microscopes. However, further studies with larger sample sizes and extended training periods are needed to validate these conclusions.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".