Clinical Practice Patterns in Tic Disorders Among Movement Disorder Society Members
Bibliographic record
Abstract
Background: Tic disorders belong to the broad spectrum of pediatric and adult movement disorders. The wide variability in clinical presentations, applied assessment tools, and treatments are poorly understood. Objectives: To map practices and knowledge base of movement disorder clinicians concerning clinical features, pathophysiology, and treatment approaches in tic disorders. Methods: A 33-item survey was developed by the Tic Disorders and Tourette syndrome Study Group members of the Movement Disorder Society. The survey was distributed to the complete society membership and included responses from 346 members, 314 of whom reported treating tic disorders. Results: Approximately one third of survey respondents (35%) frequently evaluated patients with tics. The data revealed widespread use of existing guidelines (about 70%) and screening for comorbid disorders (>90%). The most common investigations used to rule out secondary causes of tics were imaging (92%), laboratory tests (66%) and neurophysiology (38%). Functional tics were the second most common tic etiology following primary tics. Only 27% of respondents reported confidence in knowledge about tic pathogenesis. Top rated interventions to treat tics were psychoeducation, cognitive behavioral intervention for tics (CBIT) and treatment for neuropsychiatric comorbidities. Antipsychotics were ranked as the most effective pharmacologic tic intervention. Conclusions: The majority of movement disorders specialists do not frequently encounter tics. There was sparse knowledge about tic pathophysiology. Psychoeducation, CBIT, the treatment of neuropsychiatric comorbidities and use of antipsychotics emerged as the most common interventions to treat tics. These results provide insight into what will be needed to improve the diagnosis and treatment of tic disorders.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| 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".