Impacts of ADHD Symptomatology on the Response to Cognitive-Behavioural Therapy with Gilles de la Tourette Syndrome Patients
Bibliographic record
Abstract
(1) Background: Gilles de la Tourette Syndrome (TS) is a neurodevelopmental disorder characterized by motor and vocal tics. Attention deficit and hyperactivity disorder (ADHD) is a common comorbidity of TS that adds further impairment. Cognitive-behavioural therapy (CBT) has shown efficacy in treating tics, yet its effectiveness in individuals with TS and comorbid ADHD remains unclear. Also, it is suggested that ADHD characteristics like executive dysfunction and inattention could hinder the response to CBT. This study aims to compare the response to CBT for tics and its maintenance six months post-therapy among TS individuals with and without ADHD symptoms. (2) Methods: In this study, 55 TS participants who completed 14-week CBT for tics were split into high (TS+) or low (TS−) ADHD symptomatology groups. Outcomes were evaluated using the Yale Global Tic Severity Scale (YGTSS) regarding global tic severity and motor and vocal tic frequency post-CBT and at a 6-month follow-up. (3) Results: No significant group difference was found regarding improvements post-CBT (n = 55), nor the maintenance six months later (n = 45). (4) Conclusions: ADHD symptoms may not hinder the response to CBT or its maintenance, suggesting that TS individuals with ADHD symptoms may not require specialized CBT interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".