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Enhanced Design and Analysis of a Minimally Invasive Antenna for Microwave Ablation in Hepatocellular Carcinoma

2024· article· en· W4400650202 on OpenAlexaff
Maleeha Khan, Dennis D. Giannacopoulos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsMcGill University
Fundersnot available
KeywordsHepatocellular carcinomaMicrowave ablationAntenna (radio)MicrowaveAblationParabolic antennaComputer scienceMedicineTelecommunicationsCancer researchInternal medicine

Abstract

fetched live from OpenAlex

Hepatocellular carcinoma's significant mortality rates have driven the exploration of advanced ablation techniques, with microwave ablation emerging as a promising thermal method. However, in-vivo analysis feasibility is limited, necessitating precise computational models. In this work, we designed and analyzed an externally tapped intertwined helical antenna with 3T-Rings for MWA. The antenna was tested on a tissue model using the High-Frequency Structure Simulator (HFSS), and the results showed a reflection coefficient S11 of –24.36dB and a gain of -0.86dB. Temperature-dependent permittivity, electrical conductivity, and thermal conductivity along with phase change effect due to temperature reaching above 100°C are incorporated using finite element method model in COMSOL. To optimize the ablation zone size, a Taguchi L27 orthogonal array is utilized. The simulation results underscore the potential of an intertwined helical antenna for MWA applications, offering insights into enhancing treatment efficacy while minimizing damage to surrounding healthy tissue.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.056
GPT teacher head0.262
Teacher spread0.205 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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