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Record W2150226132 · doi:10.1109/iembs.2006.259519

Deflection of a Flexible Needle during Insertion into Soft Tissue

2006· article· en· W2150226132 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsDeflection (physics)BevelSoft tissueCurvatureMaterials scienceTorquePercutaneousBiomedical engineeringSurgeryPhysicsStructural engineeringEngineeringMedicineOpticsMathematics

Abstract

fetched live from OpenAlex

Accurate needle insertion into soft, inhomogeneous tissue is of practical interest because of its importance in percutaneous diagnosis and therapies. The needles used in such procedures are usually long flexible needles with bevel tips that can deflect during insertion. Deflection of the needle can not only cause misplacement of the needle tip at the target but can also cause the needle to deviate from the planned path due to the curvature created along the needle shaft. In order to reduce deflection of the needle from its path and to increase the accuracy of the needle tip placement, we have studied the relationship between the needle base forces/torques and the amount of needle deflection during needle insertion. The model proposed in this paper can be used to estimate the amount of needle deflection during insertion into relatively soft tissues. Such model may be integrated into a trajectory generation algorithm in order to increase needle tip placement accuracy.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.004
GPT teacher head0.207
Teacher spread0.203 · 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

Quick stats

Citations77
Published2006
Admission routes1
Has abstractyes

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