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Thermal Therapy, Part IV: Electromagnetic and Thermal Dosimetry

2007· review· en· W1988332932 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCritical Reviews in Biomedical Engineering · 2007
Typereview
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsInstitute of Population and Public HealthUniversity of Ottawa
Fundersnot available
KeywordsDosimetryRadiation treatment planningMedical physicsDocumentationRadiation therapyHeat transferNuclear engineeringSystems engineeringMedicineComputer scienceNuclear medicineEngineeringPhysicsRadiology

Abstract

fetched live from OpenAlex

In this article some of the important techniques in electromagnetic (EM) and thermal dosimetry are reviewed. Three major areas are discussed: modeling power deposition and estimation of EM energy absorbed by tissues exposed to EM radiation, electrical-thermal modeling for thermal therapy with various models of heat transfer in living tissues, and thermal dosimetry using invasive and noninvasive thermometry. Knowledge about the temperature distributions achieved can only be obtained by treatment planning of patient therapy. This process is called thermal therapy planning system (TTPS), which is a large and complex system for design, control, documentation, and evaluation of the treatment that also provides data for treatment optimization. Various imaging techniques for guidance and monitoring necessary for clinical treatments are also discussed. The review concludes by suggesting future avenues for investigations.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.037
GPT teacher head0.320
Teacher spread0.283 · 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