Article in Circulation. Cardiovascular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Giselle Ramirez *Artificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA (G.R., V.B., R.J.H.M., M.L., P.J.S.).
Valerie Builoff *Artificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA (G.R., V.B., R.J.H.M., M.L., P.J.S.).ORCID 0009-0006-4838-3700
Robert J H MillerArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA (G.R., V.B., R.J.H.M., M.L., P.J.S.).ORCID 0000-0003-4676-2433
Mark LemleyArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA (G.R., V.B., R.J.H.M., M.L., P.J.S.).ORCID 0000-0002-1531-880X
Isabel Carvajal-JuarezDepartment of Nuclear Cardiology, Ignacio Chavez National Institute of Cardiology, Mexico City, Mexico (I.C.-J., E.A.).ORCID 0000-0001-8764-6012
Erick AlexandersonDepartment of Nuclear Cardiology, Ignacio Chavez National Institute of Cardiology, Mexico City, Mexico (I.C.-J., E.A.).
Thomas L RosamondDepartment of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City (T.L.R.).
Na SongDepartment of Radiology (Nuclear Medicine), Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY (N.S., M.I.T., L.S.).ORCID 0000-0003-0552-712X
Mark I TravinDepartment of Radiology (Nuclear Medicine), Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY (N.S., M.I.T., L.S.).ORCID 0000-0003-0968-2143
Leandro SlipczukDepartment of Radiology (Nuclear Medicine), Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY (N.S., M.I.T., L.S.).ORCID 0000-0003-3091-3735
Andrew J EinsteinDivision of Cardiology, Departments of Medicine and Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York (A.J.E.).ORCID 0000-0003-2583-9278
Samuel B WoppererDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN (S.B.W., P.C.).
Marcelo F Di CarliDivision of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA (M.F.D.C.).ORCID 0000-0003-3119-025X
Panithaya ChareonthaitaweeDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN (S.B.W., P.C.).ORCID 0000-0002-3811-8712
Piotr J SlomkaArtificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA (G.R., V.B., R.J.H.M., M.L., P.J.S.).ORCID 0000-0002-6110-938X
Funding
Patient-specific Outcome Prediction from Cardiovascular Multimodality Imaging by Artificial IntelligenceR35HL161195 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI Piotr J Slomka · 2022 to 2026
$5.0M
Integrating Artificial Intelligence for Optimal Analysis of CardiacPET/CTR01EB034586 · NIBIB · CEDARS-SINAI MEDICAL CENTER · PI DI CARLI, MARCELO F, SLOMKA, PIOTR J · 2022 to 2025
backgroundMyocardial flow reserve (MFR), measured by positron emission tomography (PET) myocardial perfusion imaging, provides valuable information on epicardial coronary disease, diffuse atherosclerosis, and microvascular function. Despite its routine use, the prognostic efficacy of
methodsWe considered patients from 5 sites in the REFINE PET (Registry of Flow and Perfusion Imaging for Artificial Intelligence with PET) registry who underwent
resultsIn total, 6277 patients were included (median age of 65 years, 56% male). Median follow-up time was 3.8 years. There were 1895 patients with MFR ≤2 and 4382 with MFR >2. Patients with MFR ≤2 had significantly higher mortality than those with MFR >2 (n=701 [37.0%] versus n=537 [12.3%], respectively;
conclusionsIn this large multicenter cohort, MFR derived from
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Multicenter Evaluation of Myocardial Flow Reserve as a Prognostic Marker for Mortality in · full record | OpenQuestion