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Record W2165061586 · doi:10.1109/tbme.2005.846734

Needle Steering and Motion Planning in Soft Tissues

2005· article· en· W2165061586 on OpenAlexaff
Simon DiMaio, Septimiu E. Salcudean

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2005
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMotion planningDeflection (physics)Obstacle avoidanceJacobian matrix and determinantSoft tissueComputer scienceTrajectoryBiomedical engineeringEngineeringRobotArtificial intelligencePhysicsMobile robotSurgeryMathematicsClassical mechanics

Abstract

fetched live from OpenAlex

In this work, needle insertion into deformable tissue is formulated as a trajectory planning and control problem. A new concept of needle steering has been developed and a needle manipulation Jacobian defined using numerical needle insertion models that include needle deflection and soft tissue deformation. This concept is used in conjunction with a potential-field-based path planning technique to demonstrate needle tip placement and obstacle avoidance. Results from open loop insertion experiments are provided.

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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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

Citations250
Published2005
Admission routes1
Has abstractyes

Explore more

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