Sex Differences and the Interaction of Age and Sleep Issues in Neuropsychological Testing Performance across the Lifespan in an Add/Adhd Sample from the Years 1989 to 2009
Bibliographic record
Abstract
Chart review of population (9 to 80 years) neuropsychological test battery for ADHD diagnosis, questionnaires with multiple responders were evaluated in outpatient setting from 1989-2009. The focus was gender differences across age, diagnostic group (ADHD-Inattentive/ADHD plus), neuropsychological test performance, and reported sleep symptoms over the lifespan. Individuals were assigned to ADHD-I group or ADHD plus group (based upon secondary diagnosis of sleep, behavioral, emotional disturbance); ADHD not primary was excluded (brain insult, psychosis). Among these were 1,828 children (ages 9 to 14), adolescents (ages 15 to 17), and adults (ages 18 and above); 446 children (312 diagnosed ADHD-I), 218 adolescents (163 diagnosed ADHD-I), and 1,163 adults (877 ADHD-I). Sleep was problematic regardless of age, ADHD subtype, and gender. The type and number of sleep problems and fatigue were age dependent. ADHD subtype, gender, fatigue, age, and sleep (sleep onset, unrefreshing sleep, sleep maintenance) were significant variables affecting neuropsychological test performance (sequencing, cognitive flexibility, slow- and fast-paced input, divided attention, whole brain functioning). Findings suggest that ADHD involves numerous factors and symptoms beyond attention, such as sleep which interacts differently dependent upon age.
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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".