Exploring the Nature of ADHD Comorbidity in Tic Disorders
Bibliographic record
Abstract
Introduction: Tic disorders (Tics) are childhood-onset neurodevelopmental conditions, and approximately half of this population also have an ADHD diagnosis. Some of the clinical symptoms of Tics resemble ADHD which could explain the high comorbidity rate. Objectives: This research aims to investigate if the ADHD symptoms seen in children with both Tics and ADHD (Tics+) are true comorbid ADHD or a result of their Tics. Methods: Participants from a community-based study of children aged 6-18 reported their ADHD symptoms using the Strengths and Weaknesses of ADHD Symptoms and Normal Behavior Scale (SWAN). Their cognitive control was assessed using the Stop Signal Task. The symptom profiles of those with Tics and no ADHD diagnosis (Tics-, n=192), ADHD (n=2416), Tics+ (n=119) and controls (n=31908) were compared using logistic regressions. A linear regression model was used to determine if Tics+ was associated with greater cognitive control deficits, a well-established correlate of ADHD. Results: Tics+ demonstrated higher ADHD traits, specifically for hyperactive symptoms (p<0.001). 6 of 9 hyperactive symptoms were reported at significantly higher rates in Tics+ compared to ADHD. Cognitive control was equal between Tics+ and ADHD while both groups had significant deficits compared to Tics- and controls. Conclusion: Children with Tics+ have more hyperactive symptoms which were not associated with worse cognitive control than ADHD. The cognitive control deficits observed in Tics+ were consistent with a true comorbid ADHD. It is of interest to explore shared risk factors to explain the high prevalence of ADHD among this group.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".