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Record W4405129230 · doi:10.1002/mdc3.14296

Toxin for Tics: Practical Guidance for Clinicians from a Registry‐Based Naturalistic Study

2024· article· en· W4405129230 on OpenAlexaffabout
Tamara Pringsheim, Davide Martino

Bibliographic record

VenueMovement Disorders Clinical Practice · 2024
Typearticle
Languageen
FieldPsychology
TopicObsessive-Compulsive Spectrum Disorders
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTicsBotulinum toxinDiscontinuationMedicineTic disorderDoseExposure and response preventionAnesthesiaSurgeryPsychiatryInternal medicineCognition

Abstract

fetched live from OpenAlex

BACKGROUND: Botulinum toxin is a recommended treatment for tics. There is little practical guidance on the use of this treatment. OBJECTIVES: Our aim is to describe our experience using botulinum toxin injections for tics in adults. We provide information on tics treated, muscles injected, and dosages used to give practical guidance. METHODS: We analyzed data from the Calgary Adult Tic Registry on tic severity, the tics and muscles injected, and dosages. We assessed treatment length, reasons for discontinuation, and concurrent medications. RESULTS: Botulinum toxin was the most used medication for tics, received by 32 of 95 (33.7%) registry participants. Participants receiving botulinum toxin were significantly older and had significantly lower vocal tic severity and total tic severity. The most common motor tics treated were blinking, head turns, and shoulder raising. The mean length of treatment was 40.4 months. CONCLUSIONS: Botulinum toxin is an effective and well-tolerated treatment for adults with tics.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.042
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.066
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.070
GPT teacher head0.485
Teacher spread0.415 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations5
Published2024
Admission routes2
Has abstractyes

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