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Record W2078206417 · doi:10.1097/phm.0000000000000098

Effects of Vibration on Spasticity in Individuals with Spinal Cord Injury

2014· review· en· W2078206417 on OpenAlexaff
Mahsa Sadeghi, Bonita Sawatzky

Bibliographic record

VenueAmerican Journal of Physical Medicine & Rehabilitation · 2014
Typereview
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSpasticityMedicineSpinal cord injuryCINAHLPhysical medicine and rehabilitationPsycINFOPhysical therapyRehabilitationMEDLINEWhole body vibrationSpinal cordPsychiatryPsychological intervention

Abstract

fetched live from OpenAlex

The objective of this systematic review was to evaluate how whole-body vibration (WBV) or focal vibration (FV) would change spasticity in individuals with spinal cord injury (SCI). A search was conducted of MEDLINE, EMBASE, CINAHL, and PsycINFO electronic databases. A hand search was conducted of the bibliographies of articles and journals relevant to the research question. The inclusion criteria were three or more individuals, 17 yrs or older, with SCI who experience spasticity, and WBV or FV application. The evidence level of all ten identified studies (195 SCI subjects) was low on the basis of Centre for Evidence Based Medicine level of evidence. WBV (n = 1) and FV (n = 9) were applied to assess the effects of vibration on different measures of spasticity in individuals with SCI. FV application resulted in a short-term spasticity reduction lasting for a maximum of 24 hrs. Neurophysiologic measures showed H-reflex inhibition in individuals with SCI after FV application. WBV resulted in a decrease in spasticity lasting for 6-8 days after the last vibration session. WBV and FV might decrease spasticity for a short period, but no evidence-based recommendation can be drawn from the literature to guide rehabilitation medicine clinicians to manage spasticity with vibration application.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.984
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.011
GPT teacher head0.349
Teacher spread0.338 · 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

Citations67
Published2014
Admission routes1
Has abstractyes

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