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Record W2082246257 · doi:10.1016/j.pmrj.2014.09.021

Botulinum Toxin Injection Techniques for the Management of Adult Spasticity

2014· review· en· W2082246257 on OpenAlexaff
Heather W. Walker, Michael Y. Lee, Laxman Bahroo, Peter Hedera, David Charles

Bibliographic record

VenuePM&R · 2014
Typereview
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsMedtronic (Canada)
Fundersnot available
KeywordsSpasticityMedicinePhysical medicine and rehabilitationBotulinum toxinSpinal cord injuryIntensive care medicineSpinal cordAnesthesia

Abstract

fetched live from OpenAlex

Spasticity is often experienced by individuals with injury or illness of the central nervous system from etiologies such as stroke, spinal cord injury, brain injury, multiple sclerosis, or other neurologic conditions. Although spasticity may provide benefits in some patients, it more often leads to complications negatively impacting the patient. Nonpharmacologic treatment options often do not provide long-term reduction of spasticity, and systemic interventions, such as oral medications, can have intolerable side effects. The use of botulinum neurotoxin injections is one option for management of focal spasticity. Several localization techniques are available to physicians that allow for identification of the selected target muscles. These methods include anatomic localization in isolation or in conjunction with electromyography guidance, electrical stimulation guidance, or ultrasound guidance. This article will focus on further description of each of these techniques in relation to the treatment of adult spasticity and will discuss the advantages and disadvantages of each technique, as well as review the literature comparing the techniques.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.037
GPT teacher head0.335
Teacher spread0.298 · 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 designNot applicable
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

Citations60
Published2014
Admission routes1
Has abstractyes

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