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Record W17794327 · doi:10.1139/tcsme-2014-0016

KINEMATIC ANALYSES OF A NEW MEDICAL ROBOT FOR 3D VASCULAR ULTRASOUND EXAMINATION

2014· article· en· W17794327 on OpenAlexafffundvenue
Longfei Zhao, Andy Kar Wah Yen, Jonathan Coulombe, Pascal Bigras, Ilian A. Bonev

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2014
Typearticle
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsÉcole de Technologie Supérieure
FundersFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsKinematicsInverse kinematicsRobotArterial diseaseComputer scienceUltrasoundMedicineVascular diseaseArtificial intelligenceRadiologySurgeryPhysics

Abstract

fetched live from OpenAlex

Peripheral artery disease (PAD) is a common vascular disease which can have serious consequences for older people. Owing to the complexity of the vessels in the lower limbs, current PAD medical robots are not desirable to diagnose PAD in this area. The kinematic model of a novel six-axis serial-parallel robot for 3D vascular ultrasound examination of the lower limbs is presented in this paper. The prototype of the robot is described, and then the direct and inverse kinematic problems are solved in closed form.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.916
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.267
Teacher spread0.244 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations5
Published2014
Admission routes3
Has abstractyes

Explore more

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