Haptic interaction in robot‐assisted endoscopic surgery: a sensorized end‐effector
Bibliographic record
Abstract
Conventional endoscopic surgery has some drawbacks that can be addressed by using robots. The robotic systems used for surgery are still in their infancy. A major deficiency is the lack of haptic feedback to the surgeon. In this paper, the benefits of haptic feedback in robot-assisted surgery are discussed. A novel robotic end-effector is then described that meets the requirements of endoscopic surgery and is sensorized for force/ torque feedback. The endoscopic end-effector is capable of non-invasively measuring its interaction with tissue in all the degrees of freedom available during endoscopic manipulation. It is also capable of remotely actuating a tip and measuring its interaction with the environment without using any sensors on the jaws. The sensorized end-effector can be used as the last arm of a surgical robot to incorporate haptic feedback and/or to evaluate skills and learning curves of residents and surgeons in endoscopic surgery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".