Evidence map›Paper›PMID 39209237›Full record

ArticleJournal of the American Society of Echocardiography : official publication of the American Society of Echocardiography2024

Improving Prenatal Detection of Congenital Heart Disease With a Scalable Composite Analysis of 6 Fetal Cardiac Ultrasound Biometrics.

Aneela Reddy, Sara Rizvi, Anita J Moon-Grady, Rima Arnaout

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

  1. Guideline
  2. Article
  3. Article
  4. Article
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

4 authors.

Aneela ReddyDivision of Cardiology, Department of Pediatrics, University of California, San Francisco, San Francisco, California.
Sara RizviDepartment of Medicine, Division of Cardiology, University of California, San Francisco, San Francisco, California.
Anita J Moon-GradyDivision of Cardiology, Department of Pediatrics, University of California, San Francisco, San Francisco, California.
Rima ArnaoutDivision of Cardiology, Department of Pediatrics, University of California, San Francisco, San Francisco, California; Department of Medicine, Division of Cardiology, University of California, San Francisco, San Francisco, California; Department of Medicine, Division of Cardiology, Bakar Computational Health Sciences Institute, UCSF UC Berkeley Joint Program in Computational Precision Health, Department of Radiology, Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, California.

Funding

Toward efficient performance for deep learning on medical imagingR01HL150394 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Rima Arnaout · 2020 to 2026
$6.2M
NHLBI NIH HHS R01 HL150394
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Identifiers

PMID39209237
PMCPMC12121365

What OpenQuestion holds

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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.