A proof-of-principle robot with potential for the development of a hand-held tactile instrument for minimally-invasive artery cross-clamping
Bibliographic record
Abstract
One of the most common diseases of the vascular system is abdominal aortic aneurysm (AAA), for which the most definitive treatment is surgery. Minimally invasive aorta surgery is a novel method of surgery performed through small incisions and offers significant advantages including less pain, shorter hospital stay, faster patient recovery, less possibility of infection, etc. However, lack of sense of touch is the main drawback of this type of aorta surgery that would incapacitate the surgeon to exactly distinguish the aorta from its surrounding tissues which could cause various problems during the aorta cross-clamping process. One of the most important drawbacks is that it makes the aorta cross-clamping process the most time-consuming process of aortic repair surgery. The artificial tactile sensing approach is a novel method that can be used in various fields of medicine and, more specifically, in minimally invasive surgeries, where using the 'tactile sense' is not possible. In this paper, considering the present problems during aortic-repair-laparoscopy and imitating the movement of surgeons' fingers during aorta cross-clamping, a novel tactile-based artery cross-clamping robot is introduced and its function is evaluated experimentally. It is illustrated that this new tactile-based artery cross-clamping robot is well capable of dissecting an artery from its adjacent tissues in a short time with an acceptable accuracy.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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".