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Record W2151677602 · doi:10.1109/robot.2006.1641777

Robot-assisted catheter insertion using hybrid impedance control

2006· article· en· W2151677602 on OpenAlexaff
Jagadeesan Jayender, Rajni V. Patel, Suwas Nikumb

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsNational Research Council CanadaWestern University
Fundersnot available
KeywordsCatheterRobotImpedance controlComputer scienceSimulationBiomedical engineeringSurgeryEngineeringMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Angioplasty is a minimally invasive procedure wherein a catheter (a thermoplastic hollow wire) is inserted into the femoral artery and guided till it reaches a blockage in the blood vessel. There could be some potential complications arising from the conventional way of performing angioplasty, e.g., damage to the blood vessel due to excessive force of insertion and exposure of clinicians to harmful radiations and/or high levels of noise from an MRI machine. In this paper, we investigate the use of a robot manipulator (Mitsubishi PA 10-7C) to aid in the insertion of a catheter into a blood vessel. The robot controls the insertion force while the surgeon can remotely operate the robot from a safe and comfortable environment. The paper describes a hybrid impedance control scheme implemented on the Mitsubishi robot to perform simultaneous force/position control. The robot is used in experiments to insert a catheter into a test-bed by controlling the force of insertion and preventing the catheter from buckling or "bunching up". Experimental results for the insertion algorithms are shown

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations45
Published2006
Admission routes1
Has abstractyes

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