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Record W2121392200 · doi:10.1109/19.843060

Interstitial instrumentation for therapeutic ultrasonic heating: modeling the discrete blood vessels

2000· article· en· W2121392200 on OpenAlexaff
B.J. Jarosz

Bibliographic record

VenueIEEE Transactions on Instrumentation and Measurement · 2000
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsCarleton University
Fundersnot available
KeywordsInstrumentation (computer programming)Ultrasonic sensorMaterials scienceLumen (anatomy)Biomedical engineeringUltrasoundThermal conductivityFinite element methodAcousticsComputer sciencePhysicsSurgeryEngineeringComposite materialMedicineStructural engineering

Abstract

fetched live from OpenAlex

Instrumentation for interstitial ultrasound (US) heating is an important emerging technology in thermotherapy of deep seated tumors or those hard to reach by external devices. The instrumentation has special significance in case of radio-and/or chemotherapy resistant lesions. Its efficacy strongly depends on local tissue properties, especially local blood vessels. We evaluate effects of the vessels on temperature distribution elevated from basal by deposition of ultrasound energy. In the proposed model, we take into account several micron diameter vessels in proximity to the US four-applicator array. At large distances from the array, the volume is assigned a modified effective thermal conductivity. Our Finite Element Analysis of the so-defined problem shows that modelling under the assumption of constant, basal temperature across the vessels' lumen leads to erroneous results. The simulations agree best with experiments if fixed nodal temperature is applied at 60% of the lumen. We specify requirements on the array to avoid local underheating that could lead to performance failure of the instrumentation.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.576
Threshold uncertainty score0.555

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 head0.032
GPT teacher head0.246
Teacher spread0.214 · 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 designSimulation or modeling
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

Citations10
Published2000
Admission routes1
Has abstractyes

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