Evidence map›Paper›PMID 36464786›Full record

ArticleJournal of child psychology and psychiatry, and allied disciplines2023

Prospective prediction of developing internalizing disorders in ADHD.

Sarah L Karalunas, Dylan Antovich, Natalie Miller, Joel T Nigg

Open access · hybridAbstract read
In one paragraph

Article in Journal of child psychology and psychiatry, and allied disciplines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
1.9field-weighted citation impact, top 13% of its field
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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Reproducible Temperament Profiles in Adolescents With Attention-Deficit/Hyperactivity Disorder Prospectively Predict Clinical Outcomes.Clinical psychological science : a journal of the Association for Psychological Science · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. The Evolving ADHD Phenotype in the Externalizing Context.The American journal of psychiatry · 2026
    Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. 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

4 authors at 2 institutions in 1 country.

Sarah L KaralunasPsychological Sciences, Purdue University, West Lafayette, IN, USA.ORCID 0000-0003-3082-7458
Dylan AntovichPsychiatry and Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, USA.
Natalie MillerPsychiatry and Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, USA.
Joel T NiggPsychiatry and Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, USA.ORCID 0000-0003-0003-3024
Oregon Health & Science University · USPurdue University West Lafayette · US

Funding

ADHD heterogeneity, mechanisms, and risk profileR37MH059105 · NIMH · OREGON HEALTH & SCIENCE UNIVERSITY · PI NIGG, JOEL T · 2014 to 2023
$7.0M
Biobehavioral validation of the ADHD emotion dysregulation phenotypeR01MH120109 · NIMH · PURDUE UNIVERSITY · PI KARALUNAS, SARAH LYN · 2020 to 2025
$2.9M
Neurophysiological mechanisms of cognitive and emotional control in ADHDK23MH108656 · NIMH · OREGON HEALTH & SCIENCE UNIVERSITY · PI KARALUNAS, SARAH LYN · 2016 to 2019
$742k
NIMH NIH HHS K23 MH108656NIMH NIH HHS R01 MH120109NIMH NIH HHS R37 MH059105
6 · The paper itself

Abstract

backgroundClinical course in attention-deficit/hyperactivity disorder (ADHD) is highly heterogeneous with respect to both core symptoms and associated features and impairment. Onset of comorbid anxiety and mood disorders during later childhood and adolescence is one critical aspect of divergent outcomes in ADHD. Characterizing heterogeneity in onset of anxiety and depression and identifying prospective predictors of these divergent courses may facilitate early identification of the children most at risk.

methodsA total of 849 children recruited for a case-control study of ADHD development, aged 7-12 years at baseline, completed up to six annual waves of comprehensive clinical and cognitive assessment, including multi-informant behavior ratings, parent semi-structured clinical diagnostic interviews, and measures of executive function (EF). Latent class growth curve analyses (LCGAs) characterized patterns of anxiety and depression over time. Trajectories were predicted from baseline parent-rated child temperament, lab-measured child EF, coded parental criticism, and child-reported self-blame for inter-parental conflict.

resultsLatent class growth curve analyses separately identified three trajectories for anxiety and three for depression: persistently high, persistently low, and increasing. Temperamental fear/sadness and irritability were independent predictors that interacted with family characteristics. Baseline parental criticism and self-blame for inter-parental conflict exerted influence but only in the context of low temperamental risk. Better baseline child working memory was associated with delayed onset of depression.

conclusionsThe interaction of baseline child emotional features with EF or family environment predicted divergent courses of both anxiety and depression from middle-childhood to mid-adolescence. Results suggest modifiable risk factors associated with prospective differences in long-term outcomes.

Indexed as

Attention Deficit Disorder with HyperactivityAdolescentAnxietyCase-Control StudiesChildHumansIrritable MoodRisk FactorsADHDexecutive functionfamily environmentinternalizing comorbiditytemperament

Identifiers

PMID36464786
PMCPMC12004543
OpenAlexW4310709742

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.