MétaCan
Menu
Back to cohort

Observation of the Ultrasonic Vibration Potential with an Instrumented Coaxial Needle Probe

2023· article· en· W4384158756 on OpenAlexafffund
Conor McDermott, Hossein Asilian Bidgoli, Carlos Rossa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsUltrasonic sensorUltrasoundBiomedical engineeringAcousticsVibrationSIGNAL (programming language)Computer scienceMaterials scienceMedicinePhysics

Abstract

fetched live from OpenAlex

Stereotactic core biopsies typically use ultrasound imaging to locate tumours and guide a biopsy needle toward them. Due to the poor resolution of ultrasound images, oftentimes it is difficult to ensure that the needle reaches the target before the tissue is sampled resulting in false negatives. Sensor-instrumented needles provide an efficient way of characterizing the tissue at the needle tip to improve tumour targeting. A modality that has not been considered for real-time tissue characterization is the ultrasonic vibration potential (UVP) of the medium - a small electric signal generated within the tissue when subjected to ultrasonic pressure. The magnitude of the UVP depends on the electroacoustic properties of the tissue, providing valuable information about the relative tissue composition. This paper investigates, for the first time, the feasibility of measuring the ultrasonic vibration potential (UVP) of assorted media using an instrumented biopsy needle. The results show that the UVP can be effectively measured using the proposed instrumented needle providing electroacoustic data that can be used in future work for real-time tissue classification during stereotactic procedures.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.617
Threshold uncertainty score0.135

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.184
Teacher spread0.175 · 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 teacher head, 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

Citations3
Published2023
Admission routes2
Has abstractyes

Explore more

Same topicElectrical and Bioimpedance TomographyFrench-language works237,207