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Record W3084944599 · doi:10.1039/d0bm01046d

Iodinated polymer nanoparticles as contrast agent for spectral photon counting computed tomography

2020· article· en· W3084944599 on OpenAlexaff
Joëlle Balegamire, Marc Vandamme, E. Chereul, Salim Si‐Mohamed, Samira Azzouz Maache, Eyad Almouazen, Laurent Ettouati, Hatem Fessi, Loïc Boussel, Philippe Douek, Yves Chevalier

Bibliographic record

VenueBiomaterials Science · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsCanadian Nautical Research Society
FundersHorizon 2020 Framework Programme
KeywordsComputed tomographyContrast (vision)NanoparticlePolymerPhoton countingTomographyMaterials scienceSingle-photon emission computed tomographyChemistryPhotonNuclear medicineRadiologyPhysicsNuclear magnetic resonanceOpticsNanotechnologyMedicine

Abstract

fetched live from OpenAlex

by a centrifugation/redispersion step, which sets radiopacity of the contrast agent in the right range for imaging cardiovascular system and biodistribution. SPCCT 'Material Decomposition' and 'K-edge reconstruction' methods allow accurate quantification of iodine, as well as specific discrimination of iodine and gadolinium in mixed phantom samples. Intravenous injection of iodinated polymer nanoparticles to rats provides a clear visualization of the cardiovascular system over several hours followed by progressive accumulation in liver and spleen. This material is a 'blood pool' contrast agent with very long residence time in the blood stream.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.015
GPT teacher head0.235
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), 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

Citations30
Published2020
Admission routes1
Has abstractyes

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