Toward a Multifactorial Conception of the Gilles de la Tourette Syndrome and Persistent Chronic Tic Disorder
Bibliographic record
Abstract
Despite recent giant leaps in understanding Gilles de la Tourette's syndrome (now Tourette Disorder in the DSM 5), accurate multi-modal description, rigorous assessment procedures, and the improvement of evidence-based treatment currently pose a considerable challenge. In this context, the current special edition aims to elaborate three important dimensions in Tourette Disorder. Firstly, the effective characterization and etiological basis of the disorder are reviewed, since such characterization impacts accurate assessment. Secondly, subsequent articles cover the comprehensive evaluation and assessment of tic disorders, essential for treatment planning. Thirdly, the final group of articles propose novel and innovative treatment strategies for pharmacologically and behaviorally reducing tic frequency. In the current editorial address, two main issues seem crucial to the development of interventions for Tourette disorder. Primarily, integrating new technology in treatments, while supporting cognitive and behavioral recovery through learning self-controlled strategies. Additionally, the dissemination of study results to frontline resources, needs streamlining and empirically validated treatments for tic disorders should be the subject of knowledge translation to community organizations and be more widely available to the public.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".