MétaCan
Menu
Back to cohort
Record W2224378993

Abstract 18193: Stress Myocardial Perfusion Positron Emission Tomography Provides Incremental Risk Prediction in Subjects With and Without Diabetes

2014· article· en· W2224378993 on OpenAlexaff
Hicham Skali, Marcelo F. Di Carli, Ron Blankstein, Benjamin J.W. Chow, Rob Beanlands, Daniel S. Berman, James K. Min, Michael E. Merhige, Brent A. Williams, Emir Veledar, Leslee J. Shaw, Sharmila Dorbala

Bibliographic record

VenueFlorida International University Digital Commons (Florida International University) · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineMyocardial perfusion imagingPositron emission tomographyHazard ratioEjection fractionDiabetes mellitusInternal medicineCardiologyProportional hazards modelCardiac PETNuclear medicinePerfusionCoronary artery diseaseConfidence intervalHeart failureEndocrinology
DOInot available

Abstract

fetched live from OpenAlex

Introduction: The prognostic value of Positron Emission Tomography (PET) Myocardial Perfusion Imaging (MPI) is well established; however, it has not been extensively studied in subjects with diabetes mellitus (DM). We aimed to assess the prognostic value of PET MPI in subjects with and without DM. Methods: We studied 6037subjects (mean age 63±13 years, 47% women, 27% with DM, mean ejection fraction 60±16%, 33% with known coronary disease) who underwent a clinically indicated rest/stress Rubidium-82 PET MPI at 4 centers. Cardiac death (n=169) was evaluated at a mean follow-up of 2.5 ± 1.5 years. We used Cox proportional hazards models and risk reclassification measures stratified according to reported DM status. Bracco and Astellas provided initial funding for the PET registry. The current analysis is independently performed by the PET registry investigators. Results: Subjects with DM had a higher risk of cardiac death [hazard ratio (HR) (95% CI): 2.6 (1.9-3.5)] compared to those without DM. In multivariab...

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.005
GPT teacher head0.190
Teacher spread0.185 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueFlorida International University Digital Commons (Florida International University)Same topicCardiac Imaging and DiagnosticsFrench-language works237,207