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Record W2024158803 · doi:10.1118/1.598980

Magnetic resonance imaging of thermal coagulation effects in a phantom for calibrating thermal therapy devices

2000· article· en· W2024158803 on OpenAlexaff
Louis‐S. Bouchard, Michael Bronskill

Bibliographic record

VenueMedical Physics · 2000
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNational Cancer Institute
KeywordsImaging phantomMaterials scienceThermalMagnetic resonance imagingUltrasoundBiomedical engineeringMicrowaveResonance (particle physics)Nuclear magnetic resonanceRadiologyMedicineComputer scienceThermodynamicsPhysics

Abstract

fetched live from OpenAlex

A material has been developed and tested that permanently records thermal response patterns from heating devices. The material consists of a mixture of polyacrylamide and 18% w/w bovine serum albumin. Thermal denaturation is complete when the local temperature exceeds 70 degrees C, causing a large reduction in the T2 of the material. Three-dimensional distributions of "thermal damage" can be assessed using standard magnetic resonance imaging sequences. The material works well with microwave heating devices and is adaptable for use with ultrasound, radio-frequency, or laser heating devices. Suggested uses include characterizing heating devices prior to treatment and developing new clinical applications for thermal therapies.

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)0.003
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.007
GPT teacher head0.230
Teacher spread0.223 · 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
Published2000
Admission routes1
Has abstractyes

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