Obstetric complications in children with Attention Deficit/Hyperactivity Disorder and Learning Disability
Bibliographic record
Abstract
This study aims to determine whether children with ADHD and learning disabilities (LD) have a significant history of obstetrical complications when compared to children with ADHD but without LD. Methods: Sixty-four children aged 6 to 12 years diagnosed with ADHD were assessed for a history of obstetrical complications using the Kinney medical and gynecological questionnaire. Learning ability was appraised using the Wide-Range Achievement Test (WRAT-R) for anglophone students and the "Test de Rendement Français" for francophone students. Results: Children with ADHD and a learning disability in mathematics had a higher rate of neonatal complications of great severity (p = 0.01) than children with ADHD and no disability in mathematics. Children with ADHD and a learning disability in reading also had a preponderance of neonatal complications of high severity (p = 0.02) compared to their peers with ADHD and no learning disability in reading. Children with ADHD and learning disability tend to have a significant history of neonatal complications, which validates the theory that complications in early life could adversely affect a child's academic ability later in life. This further confirms the importance of the perinatal and postnatal periods in CNS development of brain regions essential for mathematics and reading ability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".