MétaCan
Menu
Back to cohort
Record W2109417679 · doi:10.1109/iembs.2007.4352330

Effects of Different Insertion Methods on Reducing Needle Deflection

2007· article· en· W2109417679 on OpenAlexafffund
Niki Abolhassani, Rajni V. Patel, Farzam Ayazi

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.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.019
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

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 designBench or experimental
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

Citations43
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueConference proceedingsSame topicSoft Robotics and ApplicationsFrench-language works237,207