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Record W2754300823 · doi:10.1109/tra.2003.817044

Needle insertion modeling and simulation

2003· article· en· W2754300823 on OpenAlexaff
Simon DiMaio, Septimiu E. Salcudean

Bibliographic record

VenueIEEE Transactions on Robotics and Automation · 2003
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHaptic technologyFinite element methodStiffnessImaging phantomDisplacement (psychology)Deformation (meteorology)Computer sciencePlanarContact forceStiffness matrixMatrix (chemical analysis)SimulationMechanicsMaterials sciencePhysicsStructural engineeringClassical mechanicsEngineeringComputer graphics (images)Optics

Abstract

fetched live from OpenAlex

A methodology for estimating the force distribution that occurs along a needle shaft during insertion is described. An experimental system for measuring planar tissue phantom deformation during needle insertions has been developed and is presented. A two-dimensional linear elastostatic material model, discretised using the finite element method, is used to derive contact force information that is not directly measurable. This approach provides a method for quantifying the needle forces and soft tissue deformations that occur during general needle trajectories in multiple dimensions. The needle force distribution is used for graphical and haptic real-time simulations of needle insertion. Since the force displacement relationship is required only along the needle, a condensation technique is shown to achieve very fast interactive simulations. Stiffness matrix changes corresponding to changes in boundary conditions and material coordinate frames are performed using low-rank matrix updates.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0100.003

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 head0.017
GPT teacher head0.230
Teacher spread0.213 · 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 designSimulation or modeling
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

Citations513
Published2003
Admission routes1
Has abstractyes

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