Evidence map›Paper›PMID 39494400›Full record

ReviewUS cardiology2024

New Innovations to Address Sudden Cardiac Arrest.

Christine P Shen, Sanjeev P Bhavnani, John D Rogers

Abstract readReview
In one paragraph

Review in US cardiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Christine P ShenDivision of Cardiology, Scripps Clinic San Diego, CA.
Sanjeev P BhavnaniDivision of Cardiology, Scripps Clinic San Diego, CA.
John D RogersDivision of Cardiology, Scripps Clinic San Diego, CA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mortality from sudden cardiac arrest remains high despite increased awareness and advancements in emergency resuscitation efforts. Various gaps exist in bystander resuscitation, automated external defibrillators, and access. Significant racial, gender, and geographic disparities have also been found. A myriad of recent innovations in sudden cardiac arrest uses new machine learning algorithms with high levels of performance. These have been applied to a broad range of efforts to identify individuals at high risk, recognize emergencies, and diagnose high-risk cardiac arrhythmias. Such technological advancements must be coupled to novel public health approaches to best implement these innovations in an equitable way. The authors propose a data-driven, technology-enabled system of care within a public health system of care to ultimately improve sudden cardiac arrest outcomes.

Indexed as

automated external defibrillatormachine learningSudden cardiac arrest

Identifiers

PMID39494400
PMCPMC11526475

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.