Tourette syndrome deep brain stimulation: A review and updated recommendations
Bibliographic record
Abstract
Deep brain stimulation (DBS) may improve disabling tics in severely affected medication and behaviorally resistant Tourette syndrome (TS). Here we review all reported cases of TS DBS and provide updated recommendations for selection, assessment, and management of potential TS DBS cases based on the literature and implantation experience. Candidates should have a Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM V) diagnosis of TS with severe motor and vocal tics, which despite exhaustive medical and behavioral treatment trials result in significant impairment. Deep brain stimulation should be offered to patients only by experienced DBS centers after evaluation by a multidisciplinary team. Rigorous preoperative and postoperative outcome measures of tics and associated comorbidities should be used. Tics and comorbid neuropsychiatric conditions should be optimally treated per current expert standards, and tics should be the major cause of disability. Psychogenic tics, embellishment, and malingering should be recognized and addressed. We have removed the previously suggested 25-year-old age limit, with the specification that a multidisciplinary team approach for screening is employed. A local ethics committee or institutional review board should be consulted for consideration of cases involving persons younger than 18 years of age, as well as in cases with urgent indications. Tourette syndrome patients represent a unique and complex population, and studies reveal a higher risk for post-DBS complications. Successes and failures have been reported for multiple brain targets; however, the optimal surgical approach remains unknown. Tourette syndrome DBS, though still evolving, is a promising approach for a subset of medication refractory and severely affected patients.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".