Systematic Review and Meta-Analysis: Predictors of Adult Psychiatric Outcomes of Childhood ADHD
Bibliographic record
Abstract
ObjectivePsychiatric disorders are highly prevalent in adults with childhood-onset attention-deficit/hyperactivity disorder (ADHD). Currently, little is known about childhood predictors for these outcomes.MethodPubMed, PsychInfo, WoS, and EMBASE were searched until June 2024. Eligible studies investigated childhood predictors of persistent ADHD, substance use disorders (SUD), conduct disorder, antisocial personality disorder, major depressive disorder (MDD), and/or anxiety disorders in adults diagnosed with childhood ADHD (PROSPERO #CRD42022320887). Meta-analytic models were tested when N ≥3 for a predictor with similar effect measures, otherwise predictors were discussed narratively when N ≥2. Newcastle-Ottawa scale was used to assess study quality. ResultsThe selected 36 studies included 119 predictors, with 10 predictors eligible for meta-analyses. History of stimulant treatment (OR = 1.88, 95% CI 1.28-2.75, p = .001) was associated with increased, and higher childhood IQ with decreased (OR = 0.99, 95% CI 0.98-1.00, p =.039), risk of ADHD persistence in adulthood. ADHD persistence was associated with increased risk of SUDs (OR = 2.12, 95% CI 1.53-3.17, p =.004) and MDD (OR = 3.19, 95% CI 1.71-5.95, p <.001). Narratively reviewed predictors of fair/good quality studies showed potential predictors for ADHD persistence (i.e., ADHD combined type, hyperactive/impulsive symptoms, anxiety disorders, externalizing problems, social dysfunctioning, and socioeconomic status). ConclusionWe confirmed earlier reported childhood predictors (i.e., stimulant treatment history, ADHD persistence) and identified potential new predictors (i.e., childhood anxiety disorders, social problems, socioeconomic status) for psychiatric outcomes of ADHD. However, the available literature is hampered by methodological shortcomings. Future studies should focus on studying combined effects of potential predictors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.041 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".