Evidence map›Paper›PMID 31425198›Full record

ArticleAnesthesia and analgesia2019

Remote Monitoring in the Perioperative Setting: Calling for Research and Innovation Ecosystem Development.

Laleh Jalilian, Maxime Cannesson, Nirav Kamdar

Abstract readEditorial
PubMed Full text at PMC Publisher
In one paragraph

Article in Anesthesia and analgesia, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Laleh JalilianFrom the Department of Anesthesiology and Perioperative Medicine, University of California, Los Angeles, Los Angeles, California.
Maxime Cannesson
Nirav Kamdar

Funding

UC BRAID Center for Accelerated Innovation COVID-19 diagnostic project developmentU54HL119893 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PALAZZOLO, MICHAEL J · 2013 to 2020
$18.3M
Machine Learning of Physiological Waveforms and Electronic Health Record Data to Predict, Diagnose, and Treat Hemodynamic Instability in Surgical PatientsR01HL144692 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CANNESSON, MAXIME · 2019 to 2023
$3.5M
Machine learning of physiological variables to predict diagnose and treat cardiorespiratory instabilityR01GM117622 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI PINSKY, MICHAEL R · 2016 to 2019
$2.4M
NHLBI NIH HHS R01 HL144692NHLBI NIH HHS U54 HL119893NIGMS NIH HHS R01 GM117622
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

InventionsAnesthesiologyBiomedical ResearchHumansPerioperative CareProgram DevelopmentRemote Sensing Technology

Identifiers

PMID31425198
PMCPMC6820340

What OpenQuestion holds

Textmetadata
LicenceTDM
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.