Decoupling: adaptation of a treatment for body-focused repetitive behaviour to Tourette syndrome. A case report
Bibliographic record
Abstract
AIMS: Tourette syndrome (TS) is a neurological condition; its etiology is not yet fully understood. Cognitive behavioural therapy with habit reversal training is the recommended first-line treatment, but is not effective in all patients. This is the first report examining the usefulness of decoupling, a behavioural self-help treatment originally developed for patients with body-focused repetitive behaviours, in a patient with TS. METHOD: Patient P.Z. showed 10 motor and three vocal tics on the Adult Tic Questionnaire (ATQ) before treatment. He was taught decoupling by the first author. RESULTS: The application of decoupling led to a reduction of P.Z.'s eye tics, which was one of his first and most enduring and severe tics. It was not effective for other areas. Quality of life and depression improved, which P.Z. attributed to the improvement of his tics. CONCLUSION: Decoupling may be adopted as an alternative, when habit reversal training is not feasible. Future research, preferably using a controlled design with a large sample, may elucidate whether decoupling is only effective for tics relating to the eyes, the most common symptom in tic disorder/TS, or whether its effects extend to other symptoms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".