Evidence map›Paper›PMID 27432845›Full record

ArticlePediatrics2016

Predictors of Poor School Readiness in Children Without Developmental Delay at Age 2.

Bergen B Nelson, Rebecca N Dudovitz, Tumaini R Coker, Elizabeth S Barnert, Christopher Biely, Ning Li, Peter G Szilagyi, Kandyce Larson, Neal Halfon, Frederick J Zimmerman and 1 more

Abstract readValidation Study
In one paragraph

Article in Pediatrics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Trial
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  3. Observational
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  15. A tutorial on calibration measurements and calibration models for clinical prediction models.Journal of the American Medical Informatics Association : JAMIA · 2020
    Review
  16. Article
  17. Precision public health-the Emperor's new clothes.International journal of epidemiology · 2019
    Article
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Bergen B NelsonDepartment of Pediatrics, Mattel Children's Hospital and Children's Discovery & Innovation Institute, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California; UCLA Center for Healthier Children, Families and Communities, Los Angeles, California; bnelson@mednet.ucla.edu.
Rebecca N DudovitzDepartment of Pediatrics, Mattel Children's Hospital and Children's Discovery & Innovation Institute, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California;
Tumaini R CokerDepartment of Pediatrics, Mattel Children's Hospital and Children's Discovery & Innovation Institute, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California; RAND Health, RAND Corporation, Santa Monica, California;
Elizabeth S BarnertDepartment of Pediatrics, Mattel Children's Hospital and Children's Discovery & Innovation Institute, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California;
Christopher BielyDepartment of Pediatrics, Mattel Children's Hospital and Children's Discovery & Innovation Institute, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California;
Ning LiDepartment of Biomathematics, University of California, Los Angeles, Los Angeles, California;
Peter G SzilagyiDepartment of Pediatrics, Mattel Children's Hospital and Children's Discovery & Innovation Institute, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California;
Kandyce LarsonAmerican Academy of Pediatrics, Elk Grove Village, Illinois, and.
Neal HalfonDepartment of Pediatrics, Mattel Children's Hospital and Children's Discovery & Innovation Institute, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California; UCLA Center for Healthier Children, Families and Communities, Los Angeles, California;
Frederick J ZimmermanDepartment of Health Policy and Management, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, California.
Paul J ChungDepartment of Pediatrics, Mattel Children's Hospital and Children's Discovery & Innovation Institute, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California; RAND Health, RAND Corporation, Santa Monica, California; Department of Health Policy and Management, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, California.

Funding

UCLA Clinical and Translational Science InstituteUL1TR000124 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI DUBINETT, STEVEN M. · 2012 to 2015
$57.0M
UCLA Clinical and Translational Science InstituteKL2TR000122 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI DUBINETT, STEVEN M. · 2012 to 2015
$4.5M
NCATS NIH HHS KL2 TR000122NCATS NIH HHS UL1 TR000124
6 · The paper itself

Abstract

BACKGROUND AND

objectivesCurrent recommendations emphasize developmental screening and surveillance to identify developmental delays (DDs) for referral to early intervention (EI) services. Many young children without DDs, however, are at high risk for poor developmental and behavioral outcomes by school entry but are ineligible for EI. We developed models for 2-year-olds without DD that predict, at kindergarten entry, poor academic performance and high problem behaviors.

methodsData from the Early Childhood Longitudinal Study, Birth Cohort (ECLS-B), were used for this study. The analytic sample excluded children likely eligible for EI because of DDs or very low birth weight. Dependent variables included low academic scores and high problem behaviors at the kindergarten wave. Regression models were developed by using candidate predictors feasibly obtainable during typical 2-year well-child visits. Models were cross-validated internally on randomly selected subsamples.

resultsApproximately 24% of all 2-year-old children were ineligible for EI at 2 years of age but still had poor academic or behavioral outcomes at school entry. Prediction models each contain 9 variables, almost entirely parental, social, or economic. Four variables were associated with both academic and behavioral risk: parental education below bachelor's degree, little/no shared reading at home, food insecurity, and fair/poor parental health. Areas under the receiver-operating characteristic curve were 0.76 for academic risk and 0.71 for behavioral risk. Adding the mental scale score from the Bayley Short Form-Research Edition did not improve areas under the receiver-operating characteristic curve for either model.

conclusionsAmong children ineligible for EI services, a small set of clinically available variables at age 2 years predicted academic and behavioral outcomes at school entry.

Indexed as

Developmental DisabilitiesLearning DisabilitiesModels, StatisticalProblem BehaviorChild, PreschoolEarly Intervention, EducationalFemaleForecastingHumansLongitudinal StudiesMaleRisk Assessment

Identifiers

PMID27432845
PMCPMC4960729

What OpenQuestion holds

Textmetadata
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.