Sociodemographic Differences Among Patients Receiving Coronary Artery Calcium Imaging vs Nongated Chest Computed Tomography Imaging
Open accessThe 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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Using existing imaging avoids barriers such as additional radiation, costs or patient visits and can identify subclinical atherosclerosis across more diverse populations.
“Using existing imaging avoids barriers such as additional radiation, costs or patient visits and can identify subclinical atherosclerosis across more diverse populations.8”
From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [8]
The paper’s conclusion (page not provided) says that quantifying coronary artery calcium on routine nongated chest CTs could identify more diverse populations at higher cardiovascular risk. Its introduction notes that dedicated calcium scans are not usually covered by insurance. The paper does not address whether reusing scans avoids additional radiation, costs, or patient visits, so those asserted benefits are not fully supported. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Arnett, D. K., Blumenthal, R. S., Albert, M. A., Buroker, A. B., Goldberger, Z. D., Hahn, E. J., Himmelfarb, C. D., Khera, A., Lloyd-Jones, D., McEvoy, J. W., et al. (2019) · Circulation · doi:10.1161/cir.0000000000000678 · Cited by 4,536 · see citing works
(2019) · doi:10.1161/cir.0000000000000677 · Cited by 4,501 · see citing works
Automated coronary calcium scoring using deep learning with multicenter external validation
Eng, D. K.-M., Chute, C., Khandwala, N., Rajpurkar, P., Long, J., Shleifer, S., Khalaf, M. H., Sandhu, A. T., Rodríguez, F., Maron, D. J., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00460-1 · Cited by 185 · see citing works
Self-citationSandhu, A. T., Rodriguez, F., Ngo, S., Patel, B. N., Mastrodicasa, D., Eng, D., Khandwala, N., Balla, S., Sousa, D., & Maron, D. J. (2023) · Circulation · doi:10.1161/circulationaha.122.062746 · Cited by 174 · see citing works
Self-citationNo-Charge Coronary Artery Calcium Screening for Cardiovascular Risk Assessment
Al-Kindi, S. G., Costa, M. A., Tashtish, N., Durieux, J., Zidar, D. A., Rashid, I., Sullivan, C., Gilkeson, R., Simon, D. I., & Rajagopalan, S. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.06.077 · Cited by 38 · see citing works
Socioeconomics of coronary artery calcium: Is it scored or ignored?
Ikram, M., & Williams, K. A., Sr. (2021) · Journal of cardiovascular computed tomography · doi:10.1016/j.jcct.2021.10.003 · Cited by 23 · see citing works
Flagged first, then by citations
1 of the 7 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Foraker, R., Sperling, L., Bratzke, L., Budoff, M., Leppert, M., Razavi, A. C., Rodriguez, F., Shapiro, M. D., Whelton, S., Wong, N. D., et al. (2025) · Circulation · doi:10.1161/cir.0000000000001382 · Cited by 44 · see citing works
Cardiovascular disease in women: traditional and sex-specific risk factors
Appelman, Y., Gulati, M., van Lennep, J. E. R., Shaw, L. J., & Merz, C. N. B. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf1001 · Cited by 30 · see citing works
Health Equity Across the Life Course
Al-Kindi, S. G., Bailey, A. L., & Douglass, P. (2024) · JACC Advances · doi:10.1016/j.jacadv.2024.101125 · Cited by 2 · see citing works
Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review
McDonald, V. J. F., Parsa, S., Mastrodicasa, D., Rodriguez, F., Langlotz, C., Maron, D. J., Sandhu, A. T., Dudum, R., & Jain, S. S. (2026) · BMJ Digital Health & AI · doi:10.1136/bmjdh-2026-000053
Self-citationGreenland, P., & Williams, K. A., Sr. (2025) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2025.01.011
Du, L., Rodríguez, F., Ray, K. K., Yang, E., & Kim, D. S. (2026) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2026.101730
- Authors
- Peng, A. W., Skye, M., Jain, S. S., Dudum, R., Maron, D. J., Din, N., Patel, B. N., Chaudhari, A. S., Sandhu, A. T., & Rodriguez, F.
- Affiliations
- Stanford Cardiovascular Institute (US)
- VA Palo Alto Health Care System (US)
- Mayo Clinic Hospital (US)
- ORCID
- Venue
- JACC: Advances
- Publisher
- Elsevier BV
- Published
- 2024-07
- Volume
- 3
- Issue
- 7
- Pages
- 100963
- ISSN
- 2772-963X (ISSN-L)
- PMC
- PMC11312303
- OpenAlex
- W4396916648
- Cited by
- 6
- References
- 7
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 1
- FWCI
- 1.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- JACC Advances (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Heart, Lung, and Blood Institute (K23HL151672, K01HL144607, R01HL169345, R01HL168188, R01HL167974)
- National Institute of Biomedical Imaging and Bioengineering (P41EB027060)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Health Sciences › Medicine › Surgery › Coronary Interventions and Diagnostics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Keywords
- sociodemographic differences, patient characteristics, cardiovascular risk assessment, coronary artery disease
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
