A comparative diagnostic study using clinical and multimodal assessment, including functional neuroimaging and oculomotricity tools, to differentiate <scp>ADHD</scp> in young patients from healthy control group
Bibliographic record
Abstract
AIM: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that may persist into adulthood, with no established objective diagnostic tool yet. This study aims to propose a multimodal objective assessment tool involving clinical assessments, functional neuroimaging, and oculomotricity measurement for ADHD in young adults. METHODS: Seventy-one medication-naïve patients and 71 healthy controls (HCs) aged 18 to 28 underwent clinical interviews, Conners' Adult ADHD Rating Scale (CAARS) questionnaire, functional near-infrared spectroscopy (fNIRS), oculomotricity task, and Conners' Continuous Performance Task (CPT) 3rd edition. Student's t-tests with Bonferroni's correction were performed to compare the performance between groups, and logistic regression was used for classification. RESULTS: ADHD patients had significantly lower frontal hemodynamic response during verbal fluency task (VFT) (P = 0.0003), more anticipatory eye movements during overlap task (P = 0.0006), higher latency (P < 0.0001), anticipatory (P < 0.0001), and errors (P < 0.0001) during anti-saccade task, as well as higher commission errors (P < 0.0001) and standard deviation in hit reaction time (HRT) (P = 0.0018). The multivariate logistic regression model featuring these seven parameters from the three objective tests (fNIRS-VFT, oculomotricity, and CPT) yielded an area under the receiver operating characteristic curve (AUC) value of 0.892 (95% confidence interval (CI): 0.840-0.944), with sensitivity and specificity of 80.28% and 84.51%, respectively. CONCLUSION: This multimodal assessment offered an accurate diagnostic tool for ADHD in young adults and laid the foundation for future machine-learning approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".