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Record W2025951470 · doi:10.1088/0031-9155/49/13/001

Multi-modality tissue-mimicking phantom for thermal therapy

2004· article· en· W2025951470 on OpenAlexaff
Mark W. McDonald, S. Lochhead, Rajiv Chopra, Michael Bronskill

Bibliographic record

VenuePhysics in Medicine and Biology · 2004
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsImaging phantomMaterials scienceBiomedical engineeringCharacterization (materials science)Magnetic resonance imagingNuclear magnetic resonanceNuclear medicineRadiologyMedicinePhysicsNanotechnology

Abstract

fetched live from OpenAlex

A tissue-mimicking phantom material has been developed for use with thermal therapy devices and techniques. This material has magnetic resonance properties (primarily T2) which change drastically upon thermal coagulation, enabling its use for device characterization and treatment verification using simple T2-weighted imaging techniques. The coagulation temperature of the phantom can be changed from 50-60 degrees C by adjusting the pH from 4.3 to 4.7. The energy absorption properties can be adjusted to match the acoustical and optical properties of tissues. T2 relaxation measurements are provided as a function of temperature, along with T2-weighted MR images to illustrate the visualization of heating patterns. A complete recipe for fabricating phantoms is provided.

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)0.001
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.215
GPT teacher head0.394
Teacher spread0.179 · 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

Citations90
Published2004
Admission routes1
Has abstractyes

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