ArticleBMC psychiatry2022
Questionnaire-based computational screening of adult ADHD.
Article in BMC psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed, 12 citations in OpenAlex.
- Validation of a criterion-based screening and triage pathway for adult ADHD: a prospective observational study of safety and operational efficiency.Frontiers in psychiatry · 2026Article
- Deep learning of conversation-based 'filmstrips' for robust Alzheimer's disease detection.npj aging · 2025Article
- A computational validation for the health concept maturity levels questionnaire.Frontiers in psychology · 2025Article
- Use of Machine Learning Models to Differentiate Neurodevelopment Conditions Through Digitally Collected Data: Cross-Sectional Questionnaire Study.JMIR formative research · 2024Article
- Psychometric properties of the Chinese version of the Quick Delay Questionnaire (C-QDQ) and ecological characteristics of reward-delay impulsivity of adults with ADHD.BMC psychiatry · 2024Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundADHD is classically seen as a childhood disease, although it persists in one out of two cases in adults. The diagnosis is based on a long and multidisciplinary process, involving different health professionals, leading to an under-diagnosis of adult ADHD individuals. We therefore present a psychometric screening scale for the identification of adult ADHD which could be used both in clinical and experimental settings.
methodWe designed the scale from the DSM-5 and administered it to n = 110 control individuals and n = 110 ADHD individuals. The number of items was reduced using multiple regression procedures. We then performed factorial analyses and a machine learning assessment of the predictive power of the scale in comparison with other clinical scales measuring common ADHD comorbidities.
resultsInternal consistency coefficients were calculated satisfactorily for TRAQ10, with Cronbach's alpha measured at .9. The 2-factor model tested was confirmed, a high correlation between the items and their belonging factor. Finally, a machine-learning analysis showed that classification algorithms could identify subjects' group membership with high accuracy, statistically superior to the performances obtained using comorbidity scales.
conclusionsThe scale showed sufficient performance for its use in clinical and experimental settings for hypothesis testing or screening purpose, although its generalizability is limited by the age and gender biases present in the data analyzed.
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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.