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Record W2109417679 · doi:10.1109/iembs.2007.4352330

Effects of Different Insertion Methods on Reducing Needle Deflection

2007· article· en· W2109417679 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueConference proceedings · 2007
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsLawson Health Research InstituteWestern University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Innovation Trust
KeywordsDeflection (physics)Materials scienceBevelInsertion lossDeflection angleBiomedical engineeringAcousticsComputer scienceOpticsStructural engineeringPhysicsEngineeringOptoelectronics

Abstract

fetched live from OpenAlex

Needle steering in medical procedures has attracted considerable attention in recent years. For example, in prostate brachytherapy, it is desired to insert a flexible beveled-tip needle with minimum deflection. To date, different methods of insertion which incorporate needle rotation about its insertion axis have been proposed in order to reduce needle deflection and target displacement. In this paper, needle deflection resulting from different methods of insertion are compared with our "model-based" method which estimates the amount of needle deflection using Euler-Bernoulli beam equations. Experiments are performed in gelatin phantoms and animal tissue. The results show that the proposed "model-based" method reduces the amount of needle deflection more than other methods. In this paper, some factors for choosing the appropriate method of insertion are also discussed.

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 score0.211
Threshold uncertainty score0.422

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 head0.019
GPT teacher head0.293
Teacher spread0.274 · 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