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Record W2076879727 · doi:10.1109/biorob.2014.6913742

A parallel Remote Center of Motion mechanism for needle-based medical interventions

2014· article· en· W2076879727 on OpenAlexaff
Mostafa Hadavand, Michael D. Naish, Rajni V. Patel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsLawson Health Research InstituteWestern University
Fundersnot available
KeywordsWorkspaceKinematicsMechanism (biology)RobotComputer scienceRobot kinematicsSimulationSurgical robotDegrees of freedom (physics and chemistry)Motion analysisComputer visionArtificial intelligenceMobile robotPhysics

Abstract

fetched live from OpenAlex

A novel parallel Remote Center of Motion (RCM) mechanism is proposed for a surgical robot designed to perform minimally invasive needle-based interventions for lung cancer diagnosis and treatment. The proposed robot provides four degrees of freedom (DOFs) to orient and move a surgical needle within a spherical coordinate system. The RCM is beneath the skin surface to minimize the invasiveness of the surgical procedure while providing the required workspace. This compact, patient-mounted robot benefits from a design capable of measuring the pure interaction forces between the needle and the tissue. In this paper, the mechanism design and its specifications are described. The kinematic analysis is presented and isotropy of the mechanism for targeting tumors is studied. Finally, the performance of the proposed robot is evaluated experimentally.

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)0.001
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.024
GPT teacher head0.266
Teacher spread0.242 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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