Prenatal and Perinatal Morbidity in Children with Tourette Syndrome and Attention-Deficit Hyperactivity Disorder
Bibliographic record
Abstract
OBJECTIVE: Tourette syndrome (TS) and attention-deficit hyperactivity disorder (ADHD) are frequently seen in combination, though the cause of comorbidity is uncertain. Low birth weight is a known risk factor for ADHD. The objective of the study was to assess the association between pre- and perinatal morbidity and the comorbid diagnosis of ADHD in children with TS. METHOD: A nested case-control study of children evaluated for TS at a subspecialty clinic was performed. Cases were defined as children with TS and ADHD; controls had TS without ADHD. Exposure to pre- and perinatal morbidity was assessed using demographic information booklets completed by parents before the diagnostic interview. RESULTS: Three hundred fifty-three children were included, 181 cases and 172 controls. Children with TS and ADHD had a greater odds of exposure to low birth weight status, prematurity, breathing problems, and maternal smoking compared with children with TS only. A multivariable logistic regression model found adjusted odds ratios for the comorbid diagnosis of TS and ADHD of 2.74 (95% CI 1.03-7.29, p = .04) in children born low birth weight, and of 2.43 (95% CI 1.23-4.82, p = .01) for children exposed to maternal smoking. CONCLUSION: In children with TS, there is a greater odds of comorbid ADHD in children born with low birth weight or with exposure to maternal smoking. The commonality of risk factors for ADHD only and tic-related ADHD supports a common underlying neurobiology. Women with fetuses at risk for TS should avoid smoking and preventable causes of low birth weight to minimize the risk of comorbid ADHD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".