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Record W4306696319 · doi:10.1007/s10554-022-02730-9

“Vascular inflammation and cardiovascular disease: review about the role of PET imaging”

2022· review· en· W4306696319 on OpenAlexaff
Antonio Maria Sammartino, Raffaele Falco, Andrea Drera, Francesco Dondi, Pietro Bellini, Francesco Bertagna, Enrico Vizzardi

Bibliographic record

VenueThe International Journal of Cardiovascular Imaging · 2022
Typereview
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsSurgical Specialties (Canada)
FundersUniversità degli Studi di Brescia
KeywordsMedicineInflammationPositron emission tomographyDiseaseInternal medicineAortaPET-CTCardiologyRadiologyPathophysiologyPathology

Abstract

fetched live from OpenAlex

Abstract Inflammation characterizes all stages of atherothrombosis and provides a critical pathophysiological link between plaque formation and its acute rupture, leading to coronary occlusion and heart attack. In the last 20 years the possibility of quantifying the degree of inflammation of atherosclerotic plaques and, therefore, also of vascular inflammation aroused much interest. 18 Fluoro-deoxy-glucose photon-emissions-tomography ( 18 F-FDG-PET) is widely used in oncology for staging and searching metastases; in cardiology, the absorption of 18 F-FDG into the arterial wall was observed for the first time incidentally in the aorta of patients undergoing PET imaging for cancer staging. PET/CT imaging with 18 F-FDG and 18 F-sodium fluoride ( 18 F-NaF) has been shown to assess atherosclerotic disease in its molecular phase, when the process may still be reversible. This approach has several limitations in the clinical practice, due to lack of prospective data to justify their use routinely, but it’s desirable to develop further scientific evidence to confirm this technique to detect high-risk patients for cardiovascular events.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.956
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.014
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.014
GPT teacher head0.275
Teacher spread0.261 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations23
Published2022
Admission routes1
Has abstractyes

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