Evidence map›Paper›PMID 30691705›Full record

ReviewClinics in chest medicine2019

Practical and Conceptual Considerations for the Primary Prevention of Asthma.

Steven M Brunwasser, Tina V Hartert

Abstract readReview
In one paragraph

Review in Clinics in chest medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

2 authors.

Steven M BrunwasserDepartment of Medicine, Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, T-1218 Medical Center North, 1161 21st Avenue South, Nashville, TN 37232-2650, USA; Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, R-1217 Medical Center North, 1161 21st Avenue South, Nashville, TN 37232-2521, USA.
Tina V HartertDepartment of Medicine, Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, T-1218 Medical Center North, 1161 21st Avenue South, Nashville, TN 37232-2650, USA. Electronic address: tina.hartert@vumc.org.

Funding

LIFE-SPAN DEVELOPMENT OF NORMAL AND ABNORMAL BEHAVIORT32MH018921 · NIMH · VANDERBILT UNIVERSITY · PI HUMPHREYS, KATHRYN LEIGH · 1989 to 2024
$6.7M
The Vanderbilt PCOR Career Knowledge, Education and Training ProgramK12HS022990 · AHRQ · VANDERBILT UNIVERSITY MEDICAL CENTER · PI PENSON, DAVID F. · 2014 to 2018
$3.3M
Midcareer Investigator Patient-oriented Research Award in Bronchiolitis & AsthmaK24AI077930 · NIAID · VANDERBILT UNIVERSITY MEDICAL CENTER · PI HARTERT, TINA V · 2008 to 2017
$1.6M
NIAID NIH HHS K24 AI077930NIMH NIH HHS T32 MH018921
6 · The paper itself

Abstract

Asthma is among the most common chronic diseases worldwide and is a significant contributor to the global health burden, highlighting the urgent need for primary prevention. This article outlines several practical and conceptual challenges that accompany primary prevention efforts. It advocates for improved predictive modeling to identify those at high-risk of developing asthma using automated algorithms within electronic medical records systems and explanatory modeling to refine understanding of causal pathways. Understanding the many issues that are likely to affect the success of primary prevention efforts helps the community of individuals invested in asthma prevention organize efforts and maximize their impact.

Indexed as

AlgorithmsAsthmaHumansPrimary PreventionAsthmaExplanatory modelsImplementationPrediction modelsPrimary prevention

Identifiers

PMID30691705
PMCPMC6355160

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.