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Record W2031894784 · doi:10.1118/1.4903898

Clinical apparatus for the reduction of dose area product for patients undergoing x‐ray catheterization

2015· article· en· W2031894784 on OpenAlexafffund
Normand Robert, Kristina N. Watt, Sophie Rochette, L. Desponds, Régis Vaillant, J. A. Rowlands

Bibliographic record

VenueMedical Physics · 2015
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsSunnybrook Health Science Centre
FundersNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchNational Institutes of HealthGeneral Electric
KeywordsIonization chamberCardiac catheterizationDose area productNuclear medicineX-rayMedicineReduction (mathematics)RadiologyBiomedical engineeringIonizationMathematicsSurgeryPhysicsRadiation doseOptics

Abstract

fetched live from OpenAlex

PURPOSE: The authors describe a design for prepatient region of interest attenuators (ROIAs) to reduce dose area product (DAP) for clinical use. The authors describe a model to predict DAP values from x-ray technique parameters recorded during a clinical procedure for image sequences obtained in the presence or absence of ROIAs. The model was developed primarily to determine what the DAP to a patient undergoing cardiac catheterization with a ROIA would have been if no ROIA had been used allowing a determination of DAP reduction. METHODS: Copper ROIAs with thicknesses that vary gradually so as not to cause significant image artifacts were constructed. X-ray image sequences were acquired on a clinical catheterization system with and without ROIAs with varying x-ray technique parameters. DAP values were measured for all said exposures using an ionization chamber and compared to a model the authors developed. RESULTS: The model can predict DAP values within 3.5% on average with or without ROIAs when compared to ionization chamber measurements. CONCLUSIONS: The proposed experimental design is adequate for measuring DAP reductions on the order of 1.5-3.5 that are expected when introducing a ROIA during patient catheterization imaging.

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)0.002
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: Empirical
Teacher disagreement score0.889
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.072
GPT teacher head0.359
Teacher spread0.288 · 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

Citations3
Published2015
Admission routes2
Has abstractyes

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