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Record W4414392439 · doi:10.1002/mdc3.70370

Anticholinergic Burden and Botulinum Toxin Needs after Deep Brain Stimulation in Adult and Pediatric Patients with Dystonia

2025· article· en· W4414392439 on OpenAlexafffundabout
Marcela Montiel, Alessandro Magliozzi, Wei Kang Lim, Carolina Gorodetsky, George M. Ibrahim, Alfonso Fasano

Bibliographic record

VenueMovement Disorders Clinical Practice · 2025
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsOntario Brain InstituteHospital for Sick ChildrenToronto Western Hospital
FundersUniversity of TorontoUniversity Health Network
KeywordsDeep brain stimulationDystoniaAnticholinergicBotulinum toxinNeurological disorderEssential tremorStimulation

Abstract

fetched live from OpenAlex

BACKGROUND: Anticholinergic medications and botulinum neurotoxin injections are established treatments for dystonia, yet they carry potential side effects and practical challenges. Deep brain stimulation (DBS) is offered in case of poor response to these approaches. OBJECTIVES: To assess the need for anticholinergic medications and botulinum neurotoxin injections in adult and pediatric patients at two specialized Canadian centers before and after DBS over the past 10 years. METHODS: 58 patients were included analyzing data before, 6 and 12 months after DBS. Clinical assessment included the Toronto Western Spasmodic Torticollis Rating Scale and Fahn-Marsden Dystonia Rating Scale. Anticholinergic burden was determined by the Anticholinergic Drug Scale (ADS). RESULTS: Severity of cervical dystonia and ADS scores reduction were statistically significant after DBS. Anticholinergic medication and Botulinum Neurotoxin injections were discontinued a year after surgery in 28.8% and 72.4% of the patients, respectively. CONCLUSION: Simplification of anti-dystonia treatments is another added benefit of DBS.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.022
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.299
Teacher spread0.292 · 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.

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

Citations2
Published2025
Admission routes3
Has abstractyes

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