Canadian Guidelines for the Evidence-Based Treatment of Tic Disorders: Behavioural Therapy, Deep Brain Stimulation, and Transcranial Magnetic Stimulation
Bibliographic record
Abstract
This clinical guideline provides recommendations for nonpharmacological treatments for tic disorders. We conducted a systematic literature search for clinical trials on the treatment of tics. One evidence-based review (including 30 studies) and 3 studies on behavioural interventions, 3 studies on deep brain stimulation (DBS), and 3 studies on transcranial magnetic stimulation (TMS) met our inclusion criteria. Based on this evidence, we have made strong recommendations for the use of habit reversal therapy and exposure and response prevention, preferably embedded within a supportive, psychoeducational program, and with the option to combine either of these approaches with pharmacotherapy. Although evidence exists for the efficacy of DBS, the quality of this evidence is poor and the risks and burdens of the procedure are finely balanced with the perceived benefits. Our recommendation is that this intervention continues to be considered an experimental treatment for severe, medically refractory tics that have imposed severe limitations on quality of life. We recommend that the procedure should only be performed within the context of research studies and by physicians expert in DBS programming and in the management of tics. There is no evidence to support the use of TMS in the treatment of tics. However, the procedure is associated with a low rate of known complications and could continue to be evaluated within research protocols. The recommendations we provide are based on current knowledge, and further studies may result in their revision in future.
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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 | 0.021 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".