The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.
Role of Cardiac Magnetic Resonance in the Diagnosis and Prognosis of Nonischemic Cardiomyopathy
Open accessAbstract
Cardiac magnetic resonance (CMR) is a valuable tool for the evaluation of patients with, or at risk for, heart failure and has a growing impact on diagnosis, clinical management, and decision-making. Through its ability to characterize the myocardium using multiple different imaging parameters, it provides insight into the etiology of the underlying heart failure and its prognosis. CMR is widely accepted as the reference standard for quantifying chamber size and ejection fraction. Additionally, tissue characterization techniques such as late gadolinium enhancement (LGE) and other quantitative parameters such as T1-mapping, both native and with measurement of extracellular volume fraction, T2-mapping, and T2-* mapping have been validated against histology in a wide range of clinical scenarios. In particular, the pattern of LGE in the myocardium can help determine the underlying etiology of the heart failure. The presence and extent of LGE determines prognosis in many of the non-ischemic cardiomyopathies. The use of CMR should increase as its utility in characterization and assessment of prognosis in cardiomyopathies is increasingly recognized. Cardiovascular magnetic resonance, cardiomyopathy, heart failure, hypertrophic cardiomyopathy, sarcoidosis, amyloidosis
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Angiotensin-neprilysin inhibition versus enalapril in heart failure
McMurray, J. J. V., Packer, M., Desai, A. S., Gong, J., Lefkowitz, M. P., Rizkala, A. R., Rouleau, J. L., Shi, V. C., Solomon, S. D., Swedberg, K., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1409077 · Cited by 6,957
Marcus, F. I., McKenna, W. J., Sherrill, D., Basso, C., Bauce, B., Bluemke, D. A., Calkins, H., Corrado, D., Cox, M. G. P. J., Daubert, J. P., et al. (2010) · Circulation · doi:10.1161/circulationaha.108.840827 · Cited by 2,676
Defibrillator implantation in patients with nonischemic systolic heart failure
Køber, L., Thune, J. J., Nielsen, J. C., Haarbo, J., Videbæk, L., Korup, E., Jensen, G., Hildebrandt, P., Steffensen, F. H., Bruun, N. E., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1608029 · Cited by 1,763
Cardiovascular T2-star (T2*) magnetic resonance for the early diagnosis of myocardial iron overload
Anderson, L. (2001) · European Heart Journal · doi:10.1053/euhj.2001.2822 · Cited by 1,682
Marcus, F. I., McKenna, W. J., Sherrill, D., Basso, C., Bauce, B., Bluemke, D. A., Calkins, H., Corrado, D., Cox, M. G. P. J., Daubert, J. P., et al. (2010) · European Heart Journal · doi:10.1093/eurheartj/ehq025 · Cited by 1,612
Birnie, D. H., Sauer, W. H., Bogun, F., Cooper, J. M., Culver, D. A., Duvernoy, C. S., Judson, M. A., Kron, J., Mehta, D., Cosedis Nielsen, J., et al. (2014) · Heart Rhythm · doi:10.1016/j.hrthm.2014.03.043 · Cited by 1,479
Self-citationNew Perspectives on the Prevalence of Hypertrophic Cardiomyopathy
Semsarian, C., Ingles, J., Maron, M. S., & Maron, B. J. (2015) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2015.01.019 · Cited by 1,384
Left Ventricular Non-Compaction
Petersen, S. E., Selvanayagam, J. B., Wiesmann, F., Robson, M. D., Francis, J. M., Anderson, R. H., Watkins, H., & Neubauer, S. (2005) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2005.03.045 · Cited by 1,242
Gulati, A., Jabbour, A., Ismail, T. F., Guha, K., Khwaja, J., Raza, S., Morarji, K., Brown, T. D. H., Ismail, N. A., Dweck, M. R., et al. (2013) · JAMA · doi:10.1001/jama.2013.1363 · Cited by 1,179
McCrohon, J. A., Moon, J. C. C., Prasad, S. K., McKenna, W. J., Lorenz, C. H., Coats, A. J. S., & Pennell, D. J. (2003) · Circulation · doi:10.1161/01.cir.0000078641.19365.4c · Cited by 1,154
Metadata
- Authors
- Patel, A. R., & Kramer, C. M.
- Venue
- JACC: Cardiovascular Imaging
- Publisher
- Elsevier BV
- Published
- 2017-10
- Volume
- 10
- Issue
- 10
- Pages
- 1180-1193
- ISSN
- 1876-7591 (ISSN-L)
- 1936-878X
- OpenAlex
- W2762195773
- Cited by
- 313
- References
- 78
- FWCI
- 15.90
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Role of Cardiac Magnetic Resonance in the Diagnosis and Prognosis of Nonischemic Cardiomyopathy. https://app.cochat.ai/papers/10.1016/j.jcmg.2017.08.005
- The paper
- Patel, A. R., & Kramer, C. M. (2017). Role of Cardiac Magnetic Resonance in the Diagnosis and Prognosis of Nonischemic Cardiomyopathy. JACC: Cardiovascular Imaging. https://doi.org/10.1016/j.jcmg.2017.08.005
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
