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Record W2119978846 · doi:10.1177/1545968309343213

Management of Spasticity After Spinal Cord Injury: Current Techniques and Future Directions

2009· review· en· W2119978846 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNeurorehabilitation and neural repair · 2009
Typereview
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsUniversity of Alberta
FundersNational Institute of Neurological Disorders and StrokeCanadian Institutes of Health Research
KeywordsSpasticitySpinal cord injuryMedicineMultiple sclerosisPhysical medicine and rehabilitationCerebral palsyStroke (engine)NeuroscienceSpinal cordPsychology

Abstract

fetched live from OpenAlex

Spasticity, resulting in involuntary and sustained contractions of muscles, may evolve in patients with stroke, cerebral palsy, multiple sclerosis, brain injury, and spinal cord injury (SCI). The authors critically review the neural mechanisms that may contribute to spasticity after SCI and assess their likely degree of involvement and relative significance to its pathophysiology. Experimental data from patients and animal models of spasticity as well as computer simulations are evaluated. The current clinical methods used for the management of spasticity and the pharmacological actions of drugs are discussed in relation to their effects on spinal mechanisms. Critical assessment of experimental findings indicates that increased excitability of both motoneurons and interneurons plays a crucial role in pathophysiology of spasticity. New interventions, including forms of spinal electrical stimulation to suppress increased neuronal excitability, may reduce the severity of spasticity and its complications.

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.

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: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.365
Teacher spread0.336 · 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