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Record W1996149024 · doi:10.1002/mrm.1288

Temperature dosimetry using MR relaxation characteristics of poly(vinyl alcohol) cryogel (PVA‐C)

2001· article· en· W1996149024 on OpenAlexaff
L.A. Lukas, Kathleen Surry, Terry M. Peters

Bibliographic record

VenueMagnetic Resonance in Medicine · 2001
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsRobarts Clinical TrialsWestern University
Fundersnot available
KeywordsVinyl alcoholDosimetryMaterials scienceRelaxation (psychology)CalibrationAnalytical Chemistry (journal)PolymerNuclear magnetic resonanceNuclear medicineComposite materialChemistryChromatographyMedicineMathematicsPhysics

Abstract

fetched live from OpenAlex

Hyperthermic therapy is being used for a variety of medical treatments, such as tumor ablation and the enhancement of radiation therapy. Research in this area requires a tool to record the temperature distribution created by a heat source, similar to the dosimetry gels used in radiation therapy to record dose distribution. Poly(vinyl alcohol) cryogel (PVA-C) is presented as a material capable of recording temperature distributions between 45 and 70 degrees C, with less than a 1 degrees C error. An approximately linear, positive relationship between MR relaxation times and applied temperature is demonstrated, with a maximum of 16.3 ms/ degrees C change in T(1) and 10.2 ms/ degrees C in T(2) for a typical PVA-C gel. Applied heat reduces the amount of cross-linking in PVA-C, which is responsible for a predictable change in T(1) and T(2) times. Temperature distributions in PVA-C volumes may be determined by matching MR relaxation times across the volumes to calibration values produced in samples subjected to known temperatures. Factors such as thermotolerance, perfusion effects, and thermal conductivity of PVA-C are addressed for potentially extending this method to modeling thermal doses in 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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.677

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.026
GPT teacher head0.327
Teacher spread0.301 · 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 designObservational
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

Citations18
Published2001
Admission routes1
Has abstractyes

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