Tics in the Pediatric Population: Pragmatic Management
Bibliographic record
Abstract
BACKGROUND: Primary tic disorders, notably Tourette syndrome, are very common movement disorders in childhood. However, the management of such patients still poses great therapeutic challenges to medical professionals. METHODS: Based on a synthesis of the available guidelines published in Europe, Canada, and the United States, coupled with more recent therapeutic developments, the authors provide a pragmatic guide to aid clinicians in deciding when and how to treat patients who have primary tic disorders. RESULTS: After a systematic assessment of tics and common neuropsychiatric comorbidities (primarily attention-deficit hyperactivity disorder [ADHD] and obsessive-compulsive disorder [OCD]), the first step in treatment is a comprehensive psychoeducation of patients and families that addresses the protean phenomenology of tics and associated behaviors, coping mechanisms, prognosis, and treatment options. When more active intervention beyond watchful monitoring is indicated, hierarchical evaluation of treatment targets (i.e., tics vs. comorbid behavioral symptoms) is crucial. Behavioral treatments for tics are restricted to older children and are not readily available to all centers, mainly due to the paucity of well-trained therapists. Pharmacological treatments, such as antipsychotics for tics, stimulants and atomoxetine for ADHD, and α2A-agonists for children with tics plus ADHD, represent widely available and effective treatment options, but safety monitoring must be provided. Combined polypharmacological and behavioral/pharmacological approaches, as well as neuromodulation strategies, remain under-investigated in this population of patients. CONCLUSIONS: The treatment of children with tics and Tourette syndrome is multifaceted. Multidisciplinary teams with expertise in neurology, psychiatry, psychology, and pediatrics may be helpful to address the complex needs of these children.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".