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Record W4402722735 · doi:10.1080/14737175.2024.2405740

Behavioural Therapy for tic disorders: a comprehensive review of the literature

2024· review· en· W4402722735 on OpenAlexaff
Simon Morand‐Beaulieu, Natalia Szejko, Julian Fletcher, Tamara Pringsheim

Bibliographic record

VenueExpert Review of Neurotherapeutics · 2024
Typereview
Languageen
FieldPsychology
TopicObsessive-Compulsive Spectrum Disorders
Canadian institutionsUniversity of CalgaryMcGill University
Fundersnot available
KeywordsPsychotherapistPsychologyTicsMedicinePsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Behavioral therapies are recommended as a first-line intervention for Tourette syndrome and persistent motor or phonic tic disorder. AREAS COVERED: In this review, the authors summarize randomized controlled trials on the comprehensive behavioral intervention for tics (CBIT), habit reversal therapy (HRT), and exposure and response prevention (ERP). Studies of face-to-face treatment, treatment by video conferencing, group treatment, and internet delivered treatment were assessed, as well as evidence of treatment predictors, modifiers, and mediators. EXPERT OPINION: There is high-quality evidence for face-to-face one-on-one treatment with CBIT, and data suggesting that one-on-one treatment by videoconference provides similar benefit. Limited data on group treatment with CBIT/HRT suggests inferiority to individual treatment, while internet-based CBIT programs appear more beneficial than wait list or psychoeducation. There is one face-to-face one-on-one treatment comparison of ERP to HRT, suggesting equal benefit. Internet-based ERP with minimal therapist support appears effective, although effect sizes are small. One study using behavioral therapy with ERP or HRT found similar benefit to medical treatment with antipsychotics. Data on predictors, modifiers, and mediators of treatment efficacy are emerging. In summary, behavioral therapies are an important treatment modality for tic disorders. Furthermore, important efforts to improve treatment accessibility are underway.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.561
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.071
GPT teacher head0.425
Teacher spread0.355 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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