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Record W2624463952 · doi:10.1002/acm2.12115

<scp>MR</scp>‐safe personal radiation dosimeters

2017· article· en· W2624463952 on OpenAlexaff
Ekaterina Tchistiakova, A. Kim, William Y. Song, Geordi Pang

Bibliographic record

VenueJournal of Applied Clinical Medical Physics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsDosimeterRadiationNuclear medicineMedical physicsRadiochemistryLow Dose RadiationDosimetryMedicinePhysicsOpticsChemistryDose–response relationshipInternal medicine

Abstract

fetched live from OpenAlex

Magnetic resonance imaging (MRI) is being rapidly integrated for cancer treatments-such systems are referred to as MRI-guided radiation therapy (MRIgRT). As the magnet of an MRI scanner is always on, the presence of a strong static magnetic field from the MRI scanner during radiotherapy delivery presents new challenges. One of the challenges is that a personal radiation dosimeter used to estimate the radiation dose deposited in an individual wearing the device must be MR-safe. No such devices, however, are currently available. In this work we first modified an existing personal dosimeter (by removing a metal clip) to make it MR-safe and then investigated potential effects of magnetic field on dosimeter readings, i.e., optically stimulated luminescent dosimeter (OSLD) readings. We found that the effect of magnetic field on OSLD sensitivity was within radiation protection tolerance levels. OSLD personal dosimeters can be directly used in conjunction with MRIgRT radiation protection purposes.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.925
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.365
Teacher spread0.339 · 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 designOther design
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

Citations8
Published2017
Admission routes1
Has abstractyes

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