The Clinical Utility of the Objective Measures for Diagnosing and Monitoring Attention-Deficit and Hyperactivity Disorder (ADHD) in Adults: A Systematic Review
Bibliographic record
Abstract
Background: Clinical practice suggests that objective assessment tools are needed to assess adults with inattention or hyperactivity, informed by the underlying pathophysiology of attention-deficit and hyperactivity disorder (ADHD). This systematic review comprehensively evaluates the current objective assessment methods as an adjunct diagnostic tool for these adults. Methods: We conducted a systematic review of studies investigating various objective diagnostic methods to assess adults with ADHD and healthy controls. The database search occurred from its inception to 23 December 2024. Results: Our search yielded 46 studies that reported on various objective methods to assess adults with ADHD. The MOXO-distracted Continuous Performance Test (MOXO-d-CPT), eye-tracker with MOXO-d CPT, Conners’ Continuous Performance Test—3rd edition (CCPT-3), and oculomotricity can differentiate between true and feigned ADHD or other diagnostic possibilities. The Quantified Behavior Test (Qb Test+) can detect hyperactivity and differentiate it from other psychiatric disorders. Mono-d, CCPT-3, Qb Test+, Test of Variables and Attention (TOVA), Integrated Visual and Auditory Continuous Performance Test (IVA-CPT), and oculomotricity can monitor pharmacotherapy response. Functional near-infrared spectroscopy (fNIRS) offers more promise than structural imaging and demonstrates a moderate level of sensitivity and specificity to differentiate adults with and without ADHD by performing the verbal fluency test. Notwithstanding, electroencephalography (EEG)/event-related potential (ERP) shows potential in diagnosis and treatment monitoring (e.g., neurofeedback training). In addition, transcriptome-based biomarkers have also been explored as diagnostic tools. Conclusion: The diagnosis and monitoring of ADHD in adults come with a unique set of challenges due to psychiatric comorbidity, including depression and anxiety; fluctuation of symptoms over time; and lack of consensus among clinicians and professional organizations to adopt objective tests in the diagnostic process. Our findings support the notion that a combination of clinical assessment and objective biomarkers targeting distinct pathophysiological aspects may enhance the accuracy of ADHD diagnosis.
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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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".