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Record W2120214714 · doi:10.1002/rcs.16

Haptic interaction in robot‐assisted endoscopic surgery: a sensorized end‐effector

2005· review· en· W2120214714 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Medical Robotics and Computer Assisted Surgery · 2005
Typereview
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsWestern UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsHaptic technologyRobot end effectorRobotic surgeryComputer scienceRobotArtificial intelligenceComputer visionSurgeryHuman–computer interactionMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.070
GPT teacher head0.356
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it