Force and torque feedback in endoscopic vessel harvesting
Bibliographic record
Abstract
PURPOSE: Endoscopic vessel harvesting is the preferred minimally invasive approach to obtain grafts for coronary bypass surgeries, however it requires extensive practice to minimize vessel damage. We propose to create a surgical training simulation with visual and haptic feedback. In this study, we focus on analyzing the force and torque peaks on the surgical retractor during the procedure. METHODS: The original retractor handle was 3D scanned and modified to attach an ATI Mini40 force-torque transducer. The forces and torques in two radial artery and two saphenous vein procedures in human cadavers were recorded. The measurements, endoscopic video and surgical surface video were collected. The median and interquartile range of the force and torque peaks were calculated for the artery and vein harvesting procedures. RESULTS: The median and interquartile range for saphenous vein harvests was larger than radial artery harvests. The largest median force and torque generated in the vein was 11.654 N [posterior] and 0.661 Nm [- frontal], whereas in the artery was 6.163 N [anterior] and 0.381 Nm [+ frontal], respectively. CONCLUSION: The distribution of force and torque peaks in the retractor was found for endoscopic vessel harvests. This data can be used to design a haptic user interface, and to establish expert benchmarks for learning curve evaluation.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".