Abstract WP180: Measuring Tissue Motion During Carotid Endarterectomy Using Video-based Analyses
Bibliographic record
Abstract
Background: During carotid endarterectomies (CEA), perioperative stroke and cranial or cervical nerve injuries are the most common major complications. Many experts have emphasized the importance of careful and gentle manipulation around carotid plaques. However, there have been no methodological assessments that quantitatively measure ‘gentleness’. This study was aimed to propose a novel metric for gentle surgical maneuvers during CEA. Methods: Using surgical video-based motion software, the motion of the carotid artery around plaque was analyzed and quantified during a CEA. Kinematic parameters (minimum and maximum acceleration, and maximum and mean velocity) were compared among the surgical tasks and techniques, as well as between novice and expert surgeons. Results: The surgical tasks of dissecting the common carotid artery, passing the proximal vessel loops, and ligating vessels showed the highest absolute values of kinematic parameters. Dissections perpendicular to the line of the internal carotid artery tended to show higher kinematic parameters than those in the parallel direction, with blunt dissections typically higher than sharp dissections. The kinematic parameters of novice surgeons were significantly higher than those of experts, and receiver operating curve analysis showed a strong discriminative power. The kinematic parameters in the case of postoperative ischemic stroke also showed the highest absolute values. Conclusions: This study shows that tissue motion parameters could be a novel and feasible surrogate marker for the objective assessment on the ‘gentleness’ of surgical performance in CEA. Such an objective measurement might be applicable towards enhancing surgical education and risk management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".