Evidence map›Paper›PMID 38555387›Full record

ArticleNPJ digital medicine2024

Contemporary attitudes and beliefs on coronary artery calcium from social media using artificial intelligence.

Sulaiman Somani, Sujana Balla, Allison W Peng, Ramzi Dudum, Sneha Jain, Khurram Nasir, David J Maron, Tina Hernandez-Boussard, Fatima Rodriguez

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2024. 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
2.7field-weighted citation impact, top 10% of its field
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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 1 country.

Sulaiman SomaniDepartment of Medicine, Stanford University, Stanford, CA, USA.
Sujana BallaDepartment of Medicine, University of California, San Francisco-Fresno, Fresno, CA, USA.
Allison W PengDepartment of Medicine, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-1801-6815
Ramzi DudumCardiovascular Institute, Stanford University, Stanford, CA, USA.
Sneha JainCardiovascular Institute, Stanford University, Stanford, CA, USA.
Khurram NasirDivision of Cardiovascular Prevention and Wellness, Department of Cardiology, Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, USA.
David J MaronCardiovascular Institute, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-4867-8588
Tina Hernandez-BoussardDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-6553-3455
Fatima RodriguezCardiovascular Institute, Stanford University, Stanford, CA, USA. frodrigu@stanford.edu.ORCID http://orcid.org/0000-0002-5226-0723
Cardiovascular Institute of the South · USHouston Methodist · USStanford University · USUniversity of California, San Francisco · US

Funding

Adherence Determinants in the Health Electronic Record Evaluation of Statins (ADHERES)R01HL168188 · NHLBI · STANFORD UNIVERSITY · PI Fatima Rodriguez · 2024 to 2026
$2.1M
SURPASS: (Statin Use and Risk Prediction of Atherosclerotic Cardiovascular Disease in minority Subgroups)K01HL144607 · NHLBI · STANFORD UNIVERSITY · PI RODRIGUEZ, FATIMA · 2019 to 2023
$856k
NHLBI NIH HHS K01 HL144607NHLBI NIH HHS R01 HL168188
6 · The paper itself

Abstract

Coronary artery calcium (CAC) is a powerful tool to refine atherosclerotic cardiovascular disease (ASCVD) risk assessment. Despite its growing interest, contemporary public attitudes around CAC are not well-described in literature and have important implications for shared decision-making around cardiovascular prevention. We used an artificial intelligence (AI) pipeline consisting of a semi-supervised natural language processing model and unsupervised machine learning techniques to analyze 5,606 CAC-related discussions on Reddit. A total of 91 discussion topics were identified and were classified into 14 overarching thematic groups. These included the strong impact of CAC on therapeutic decision-making, ongoing non-evidence-based use of CAC testing, and the patient perceived downsides of CAC testing (e.g., radiation risk). Sentiment analysis also revealed that most discussions had a neutral (49.5%) or negative (48.4%) sentiment. The results of this study demonstrate the potential of an AI-based approach to analyze large, publicly available social media data to generate insights into public perceptions about CAC, which may help guide strategies to improve shared decision-making around ASCVD management and public health interventions.

Identifiers

PMID38555387
PMCPMC10981728
OpenAlexW4393341137

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.