Evidence map›Paper›PMID 33688661›Full record

ArticlemedRxiv : the preprint server for health sciences2021

SWIFT: A Deep Learning Approach to Prediction of Hypoxemic Events in Critically-Ill Patients Using SpO

Akshaya V Annapragada, Joseph L Greenstein, Sanjukta N Bose, Bradford D Winters, Sridevi V Sarma, Raimond L Winslow

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2021. 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

5 · Who and what money

Authors and funding

6 authors.

Akshaya V AnnapragadaJohns Hopkins University School of Medicine, Baltimore, United States.
Joseph L GreensteinInstitute for Computational Medicine, The Johns Hopkins University, Baltimore, United States.
Sanjukta N BoseInstitute for Computational Medicine, The Johns Hopkins University, Baltimore, United States.
Bradford D WintersAnesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Sridevi V SarmaInstitute for Computational Medicine, The Johns Hopkins University, Baltimore, United States.
Raimond L WinslowInstitute for Computational Medicine, The Johns Hopkins University, Baltimore, United States.

Funding

Medical Scientist Training ProgramT32GM136577 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI ANDREA L COX · 2020 to 2026
$13.4M
NIGMS NIH HHS T32 GM136577
6 · The paper itself

Abstract

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO

Identifiers

PMID33688661
PMCPMC7941630

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.