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Record W2137240018 · doi:10.1109/iembs.2007.4352901

Novel Lead Configurations for Robust Bio-Impedance Acquisition

2007· article· en· W2137240018 on OpenAlexaff
Joel Ironstone, M. Graovac, James Martens, Martin Rozee, K.C. Smith

Bibliographic record

VenueConference proceedings · 2007
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsUniversity of WaterlooUniversity of TorontoTetra Tech (Canada)
Fundersnot available
KeywordsMultiplexerCapacitive sensingElectrical impedanceMultiplexingComputer scienceElectronic engineeringShunt (medical)Focused Impedance MeasurementFidelityImaging phantomBiomedical engineeringMedical physicsElectrical engineeringEngineeringMedicineTelecommunicationsSurgeryRadiology

Abstract

fetched live from OpenAlex

This paper describes a diagnostic medical instrument that has undergone a multi-centre 6500-patient clinical trial to evaluate its effectiveness as a replacement for screening X-Ray mammography. The device uses a single bipolar current source multiplexed to two 32-element electrode arrays. The multiplexer allows three novel lead configurations to be measured in addition to the common tetrapolar configuration used in many impedance-acquisition systems. These additional configurations allow pre-testing of electrode contacts, post-testing assessment of measurement fidelity and correction for the effect of shunt-capacitive pathways. The results of the clinical trial show that the device can be operated reliably by individuals with only one day of specialized training and that the device can distinguish between normal and diseased breasts.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.028
GPT teacher head0.238
Teacher spread0.210 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

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