Attention-Deficit Hyperactivity Disorder (ADHD) and narrative discourse in older adults
Bibliographic record
Abstract
Attention-Deficit Hyperactivity Disorder (ADHD) encompasses other symptoms besides inattention, hyperactivity, and impulsivity, such as language problems. ADHD can have a non-remitting course and is also found in older individuals, although there are no studies on language problems in elderly individuals with the disorder. OBJECTIVE: To investigate the presence of language impairment in older adults with ADHD. METHODS: Language impairment was investigated in three older ADHD adults, and compared with two matched control subjects using a narrative discourse task. The transcript discourses were evaluated based on the Trabasso Model for discourse analysis, and then processed by the Speech Graph Analysis software. RESULTS: Compared to control subjects, ADHD patient discourse had more Plot components and their networks exhibited more Edges. The patients had higher scores on the Narrative Inefficiency, Density and Diameter Indexes as well as on the Average Clustering Coefficient. The networks of control subjects were sequential, with little or no recursiveness, whereas those of ADHD subjects were convoluted. CONCLUSION: Our results suggest that language deficits described in children, adolescents and young adults with ADHD may persist in older adults with the disorder.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".