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Record W2314152964 · doi:10.1097/hp.0b013e31827e10f3

Characterization of MOSFET Detectors for In Vivo Dosimetry in Interventional Radiology and for Dose Reconstruction in Case of Overexposure

2013· article· en· W2314152964 on OpenAlexaboutno aff
C. Bassinet, C. Huet, C. Étard, Jean-Luc Réhel, G. Boisserie, J. Debroas, Bernard Aubert, I. Clairand

Bibliographic record

VenueHealth Physics · 2013
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsMOSFETDosimetryDosimeterDetectorNuclear medicineImaging phantomMaterials scienceMedicineSemiconductor detectorMedical physicsBiomedical engineeringTransistorPhysicsOpticsVoltage

Abstract

fetched live from OpenAlex

As MOSFET (Metal Oxide Semiconductor Field Effect Transistor) detectors allow dose measurements in real time, the interest in these dosimeters is growing. The aim of this study was to investigate the dosimetric properties of commercially available TN-502RD-H MOSFET silicon detectors (Best Medical Canada, Ottawa, Canada) in order to use them for in vivo dosimetry in interventional radiology and for dose reconstruction in case of overexposure. Reproducibility of the measurements, dose rate dependence, and dose response of the MOSFET detectors have been studied with a Co source. Influence of the dose rate, frequency, and pulse duration on MOSFET responses has also been studied in pulsed x-ray fields. Finally, in order to validate the integrated dose given by MOSFET detectors, MOSFETs and TLDs (LiF:Mg,Cu,P) were fixed on an Alderson-Rando phantom in the conditions of an interventional neuroradiology procedure, and their responses have been compared. The results of this study show the suitability of MOSFET detectors for in vivo dosimetry in interventional radiology and for dose reconstruction in case of accident, provided a well-corrected energy dependence, a pulse duration equal to or higher than 10 ms, and an optimized contact between the detector and the skin of the patient are achieved.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.195

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.025
GPT teacher head0.329
Teacher spread0.304 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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