MétaCan
Menu
Back to cohort
Record W2060732455 · doi:10.1007/s12410-011-9074-8

Radionuclide Imaging of Viable Myocardium: Is it Underutilized?

2011· article· en· W2060732455 on OpenAlexafffund
Ilias Mylonas, Rob Beanlands

Bibliographic record

VenueCurrent Cardiovascular Imaging Reports · 2011
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversity of Ottawa
FundersUniversity of OttawaLantheus Medical ImagingHeart and Stroke Foundation of Canada
KeywordsMedicineRevascularizationHibernating myocardiumModalitiesCoronary artery diseaseCardiologyRadionuclide imagingInternal medicineRadiologyMyocardial infarction

Abstract

fetched live from OpenAlex

Coronary artery disease is the major cause of heart failure in North America. Viability assessment is important as it aims to identify patients who stand to benefit from coronary revascularization. Radionuclide modalities currently used in the assessment of viability include (201)Tl SPECT, (99m)Tc-based SPECT imaging, and (18)F-fluorodexoyglucose ((18)F-FDG)-PET imaging. Different advances have been made in the last year to improve the sensitivity and specificity of these modalities. In addition, the optimum amount of viable (yet dysfunctional) myocardium is important to identify in patients, as a risk-benefit ratio must be considered. Patients with predominantly viable/hibernating myocardium can benefit from revascularization from a mortality and morbidity standpoint. However, in patients with minimal viability (predominantly scarred myocardium), revascularization risk may certainly be too high to justify revascularization without expected benefit. Understanding different radionuclide modalities and new developments in the assessment of viability in ischemic heart failure patients is the focus of this discussion.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.003
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 head0.041
GPT teacher head0.286
Teacher spread0.246 · 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 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

Citations14
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueCurrent Cardiovascular Imaging ReportsSame topicCardiac Imaging and DiagnosticsFrench-language works237,207