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Record W2034434800 · doi:10.3138/ptc.2013-73

Physical Therapy for an Adult with Chronic Stroke after Botulinum Toxin Injection for Spasticity: A Case Report

2014· article· en· W2034434800 on OpenAlexaffvenue
Shannon Gallagher, Chetan P. Phadke, Farooq Ismail, Chris Boulias

Bibliographic record

VenuePhysiotherapy Canada · 2014
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsUniversity of TorontoYork UniversityWest Park Healthcare Centre
Fundersnot available
KeywordsSpasticityMedicineStroke (engine)RehabilitationPhysical medicine and rehabilitationBotulinum toxinPhysical therapyChronic strokeBalance (ability)Intervention (counseling)GaitSurgery

Abstract

fetched live from OpenAlex

PURPOSE: In this case report, we describe the type and duration of a physical therapy and botulinum toxin type A (BoNTA) intervention directed at lower limb spasticity and the gait and balance improvement in a patient post-stroke. Treatment of focal spasticity with BoNTA intramuscular injections combined with physical therapy is recommended by rehabilitation experts. However, the optimal type and duration of physical therapy intervention to optimize any functional gains that follow chemodenervation induced by BoNTA has not been established. METHOD: One individual with chronic stroke who received BoNTA injections for upper and lower extremity spasticity was included. Physical therapy intervention consisted of 45- to 60-min sessions twice weekly for 12 weeks, based on the Bobath-neurodevelopmental therapy approach, and an activity-based home program. RESULTS: After BoNTA injections and physical therapy, the patient made clinically significant improvements in balance and gait speed and became more independent with his ambulation. CONCLUSIONS: This case report demonstrates that physical therapy after BoNTA injections can result in significant functional improvements for individuals with spasticity after chronic stroke that may not be possible with BoNTA injections alone.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score0.943

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 head0.009
GPT teacher head0.268
Teacher spread0.259 · 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 designOther design
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

Citations7
Published2014
Admission routes2
Has abstractyes

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