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Record W2547959073 · doi:10.1088/0031-9155/61/23/8199

Imaging study of using radiopharmaceuticals labeled with cyclotron-produced<sup>99m</sup>Tc

2016· article· en· W2547959073 on OpenAlexafffund
Xinchi Hou, Jesse Tanguay, Milan Vuckovic, K. Buckley, Paul Schaffer, François Bénard, T.J. Ruth, A. Ćeller

Bibliographic record

VenuePhysics in Medicine and Biology · 2016
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsTRIUMFBC Cancer AgencyUniversity of British Columbia
FundersNational Research Council CanadaNatural Resources CanadaTRIUMF
KeywordsCyclotronImpurityRadiochemistryIrradiationNuclear medicineIsotopeRadiationMaterials scienceChemistryMedicinePhysicsNuclear physicsIon

Abstract

fetched live from OpenAlex

Mo could lead to the production of other radioactive technetium isotopes (Tc-impurities) which cannot be chemically separated. Thus, these impurities could contribute to patient dose and affect image quality. The potential radiation dose caused by these Tc-impurities produced using different targets, irradiation conditions, and corresponding to different injection times have been investigated, leading us to create dose-based limits of these parameters for producing clinically acceptable CPTc. However, image quality has been not considered. The aim of the present work is to provide a comprehensive and quantitative analysis of image quality for CPTc. The impact of Tc-impurities in CPTc on image resolution, background noise, and contrast is investigated by performing both Monte-Carlo simulations and phantom experiments. Various targets, irradiation, and acquisition conditions are employed for investigating the image-based limits of CPTc production parameters. Additionally, the relationship between patient dose and image quality of CPTc samples is studied. Only those samples which meet both dose- and image-based limits should be accepted in future clinical studies.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.359

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 head0.288
GPT teacher head0.472
Teacher spread0.184 · 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 designBench or experimental
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

Citations10
Published2016
Admission routes2
Has abstractyes

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