ArticleEuropean child & adolescent psychiatry2026
From childhood to adulthood: an attempt to model long-term outcomes of ADHD using machine learning.
Article in European child & adolescent psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundChildhood-onset attention-deficit/hyperactivity disorder (ADHD) persists in 28% to 58% of individuals, and is associated with adverse long-term outcomes, like additional psychiatric diagnoses and lower educational attainment. Knowing early life predictors for adverse long-term outcomes might help develop preventive measures, but too few have been identified using conventional statistical models. The current study investigated the potential of machine learning in identifying predictors for ADHD persistence and long-term functioning of adults with childhood-onset ADHD.
methodA total of 133 adults (M
resultsPerformance of all models for ADHD persistence and functioning (quality of life) was low. Best performing models included the logistic regression model with resampling for ADHD persistence (AUC's
conclusionsFindings highlight the complexity of predicting long-term outcomes of ADHD. Model improvement is crucial before models can inform clinical practice, e.g., by increasing sample size and investigating additional predictors.
Indexed as
Identifiers
42873248What OpenQuestion holds
Registered trials
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.