Evidence map›Paper›PMID 36407451›Full record

ReviewFrontiers in cardiovascular medicine2022

Cardio oncology: Digital innovations, precision medicine and health equity.

Diego Sadler, Tochukwu Okwuosa, A J Teske, Avirup Guha, Patrick Collier, Rohit Moudgil, Abdullah Sarkar, Sherry-Ann Brown

Abstract readReview
In one paragraph

Review in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–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.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. Article
  6. Review
  7. Review
  8. Review
  9. Cardio-oncology: chances and challenges.Basic research in cardiology · 2025
    Review
  10. Article
  11. 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

8 authors.

Diego SadlerCardio Oncology Section, Department of Cardiovascular Medicine, Heart Vascular and Thoracic Institute, Cleveland Clinic Florida, Weston, FL, United States.
Tochukwu OkwuosaDivision of Cardiology, Department of Medicine, Rush University Medical Center, Chicago, IL, United States.
A J TeskeDivision of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
Avirup GuhaDivision of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA, United States.
Patrick CollierCleveland Clinic, Cardio Oncology, Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland, OH, United States.
Rohit MoudgilCleveland Clinic, Cardio Oncology, Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland, OH, United States.
Abdullah SarkarCardio Oncology Section, Department of Cardiovascular Medicine, Heart Vascular and Thoracic Institute, Cleveland Clinic Florida, Weston, FL, United States.
Sherry-Ann BrownDivision of Cardiology, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid emergence of cardio-oncology has resulted in a rapid growth of cardio-oncology programs, dedicated professional societies sections and committees, and multiple collaborative networks that emerged to amplify the access to care in this new subspecialty. However, most existing data, position statements and guidelines are limited by the lack of availability of large clinical trials to support these recommendations. Furthermore, there are significant challenges regarding proper access to cardio-oncology care and treatment, particularly in marginalized and minority populations. The emergence and evolution of personalized medicine, artificial intelligence (AI), and machine learning in medicine and in cardio-oncology provides an opportunity for a more targeted, personalized approach to cardiovascular complications of cancer treatment. The proper implementation of these new modalities may facilitate a more equitable approach to adequate and universal access to cardio-oncology care, improve health related outcomes, and enable health care systems to eliminate the digital divide. This article reviews and analyzes the current status on these important issues.

Indexed as

artificial intelligence (AI)cardio-oncologydigital technologyhealth disparitieshealth equity (MeSH)precision medicine

Identifiers

PMID36407451
PMCPMC9669068

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.