Evidence map›Paper›PMID 34210844›Full record

ReviewPediatrics2021

Creative Approaches for Assessing Long-term Outcomes in Children.

Ann Chen Wu, Corina Graif, Shannon Gwin Mitchell, John Meurer, Kenneth D Mandl

Abstract readReview
In one paragraph

Review in Pediatrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
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

5 authors.

Ann Chen WuCenter for Healthcare Research in Pediatrics, Department of Population Medicine, Harvard Medical School, Harvard University and Harvard Pilgrim Health Care, Boston, Massachusetts ann.wu@childrens.harvard.edu.
Corina GraifDepartment of Sociology and Criminology, Population Research Institute, Pennsylvania State University, University Park, Pennsylvania.
Shannon Gwin MitchellFriends Research Institute, Inc, Baltimore, Maryland.
John MeurerDivision of Community Health, Medical College of Wisconsin, Milwaukie, Wisconsin.
Kenneth D MandlComputational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts.

Funding

Population Research InstituteR24HD041025 · NICHD · PENNSYLVANIA STATE UNIVERSITY-UNIV PARK · PI LANDALE, NANCY S · 2001 to 2016
$9.7M
The Population Research Institute: Development CoreP2CHD041025 · NICHD · PENNSYLVANIA STATE UNIVERSITY, THE · PI Brian Thiede · 2016 to 2026
$9.0M
Instrumenting the Delivery System for a Genomics Research Information CommonsU01TR002623 · NCATS · BOSTON CHILDREN'S HOSPITAL · PI MANDL, KENNETH D. · 2019 to 2024
$8.6M
Precision Medicine Policy and Treatment (PreEMPT) Model IIR01HD090019 · NICHD · HARVARD PILGRIM HEALTH CARE, INC. · PI Kurt Derek Christensen · 2017 to 2026
$5.7M
Age-Dependent Pharmacogenomics of Asthma Treatment (ADAPT)R01HD085993 · NICHD · HARVARD PILGRIM HEALTH CARE, INC. · PI WU, ANN CHEN · 2016 to 2020
$3.1M
Big Data and Network Analysis of Children's HealthK01HD093863 · NICHD · PENNSYLVANIA STATE UNIVERSITY, THE · PI GRAIF, CORINA · 2017 to 2021
$692k
NCATS NIH HHS U01 TR002623NICHD NIH HHS K01 HD093863NICHD NIH HHS P2C HD041025NICHD NIH HHS R01 HD085993NICHD NIH HHS R01 HD090019NICHD NIH HHS R24 HD041025
6 · The paper itself

Abstract

Advances in new technologies, when incorporated into routine health screening, have tremendous promise to benefit children. The number of health screening tests, many of which have been developed with machine learning or genomics, has exploded. To assess efficacy of health screening, ideally, randomized trials of screening in youth would be conducted; however, these can take years to conduct and may not be feasible. Thus, innovative methods to evaluate the long-term outcomes of screening are needed to help clinicians and policymakers make informed decisions. These methods include using longitudinal and linked-data systems to evaluate screening in clinical and community settings, school data, simulation modeling approaches, and methods that take advantage of data available in the digital and genomic age. Future research is needed to evaluate how longitudinal and linked-data systems drawing on community and clinical settings can enable robust evaluations of the effects of screening on changes in health status. Additionally, future studies are needed to benchmark participating individuals and communities against similar counterparts and to link big data with natural experiments related to variation in screening policies. These novel approaches have great potential for identifying and addressing differences in access to screening and effectiveness of screening across population groups and communities.

Indexed as

CreativityPopulation HealthAdolescentArtificial IntelligenceChildComputer SimulationEducationGenomicsHumansLongitudinal StudiesMass ScreeningTime FactorsTreatment Outcome

Identifiers

PMID34210844
PMCPMC8287841

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.