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Baclofen and Botulinum Toxin A Use in Tone Management for Children With Cerebral Palsy: A Review of Current Literature and Research Gaps in Pre- and Perioperative Care

2025· review· en· W4413124226 on OpenAlexaff
Michael X. Li, Kate Wortley, Ram A. Mishaal

Bibliographic record

VenuePediatric Neurology · 2025
Typereview
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsBC Children's HospitalSunny Hill Health Centre for ChildrenUniversity of British Columbia
Fundersnot available
KeywordsCerebral palsyBaclofenBotulinum toxinMedicineTone (literature)PeriAudiologyAnesthesiaPsychologyPhysical medicine and rehabilitationInternal medicineAgonist

Abstract

fetched live from OpenAlex

Cerebral palsy (CP) is a leading cause of motor disability in children. Many children with CP have hypertonia, and some will require orthopedic surgery. Botulinum toxin A (BoNT-A), a muscle relaxant, is commonly pre- or perioperatively injected to improve surgical outcomes and reduce postoperative pain and muscle tone. However, inconsistent evidence supporting its efficacy, potential research bias from industry sponsorship, and numerous adverse effects, such as long-term changes to muscle morphology, highlight the need for a better alternative. Preoperatively increasing the dose of oral baclofen, a first-line treatment for generalized CP-related hypertonia, may improve surgical outcomes with fewer long-term adverse effects. To date, the impact of an increased oral baclofen dose for this purpose has not yet been studied. This article reviews the current evidence on the effectiveness and safety of the three more commonly used antispastic treatments, BoNT-A, oral baclofen, and intrathecal baclofen, with the goal of trialing increased oral baclofen dose as an adjunct to surgery.

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 score0.576
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
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 head0.029
GPT teacher head0.376
Teacher spread0.347 · 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
Published2025
Admission routes1
Has abstractyes

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