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Record W1984734261 · doi:10.1310/tsr1605-367

Gait Changes Following Botulinum Toxin A Treatment in Stroke

2009· article· en· W1984734261 on OpenAlexaff
Alison C. Novak, Sandra J. Olney, Stephen D. Bagg, Brenda Brouwer

Bibliographic record

VenueTopics in Stroke Rehabilitation · 2009
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsProvidence Health CareQueen's University
Fundersnot available
KeywordsAnkleGaitBotulinum toxinPhysical medicine and rehabilitationSpasticMedicineRange of motionStroke (engine)Gait analysisPhysical therapyCerebral palsyAnesthesiaSurgery

Abstract

fetched live from OpenAlex

PURPOSE: To characterize the effects of botulinum toxin A treatment of spastic plantar flexors in stroke on joint mobility and gait kinematics and kinetics. METHOD: Nine patients with hemiparetic stroke presenting with ankle hypertonicity participated in this exploratory open-label case series study. Comprehensive gait analysis provided bilateral kinematic and kinetic information for the ankle, knee, and hip joints throughout the stance phase. Data were obtained at baseline, 2 weeks, and 10 weeks post botulinum toxin injection of the spastic plantar flexors. RESULTS: Passive ankle range of motion increased post injection (p < .05). The amount of plantarflexion in late stance was significantly reduced (p < .05) while the maximum dorsiflexion increased in midstance at 10 weeks post treatment. The angular displacement profiles for the knee revealed that patients tended to display less hyperextension following treatment (p = .053). No significant changes in kinetic measures were found; however, case-by-case observations suggested that most patients experienced improvements in positive work production. CONCLUSIONS: The findings indicate that botulinum toxin treatment results in improved joint mobility and ankle kinematics and, in some patients, increases in positive work, suggesting better gait performance.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.295
Teacher spread0.277 · 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

Citations39
Published2009
Admission routes1
Has abstractyes

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