MétaCan
Menu
Back to cohort

Evaluation of therapeutic electrical stimulation to improve muscle strength and function in children with types II/III spinal muscular atrophy

2002· article· en· W4239987909 on OpenAlexaff
Darcy Fehlings, Susan Kirsch, Alan J. McComas, Kent A. Campbell

Bibliographic record

VenueDevelopmental Medicine & Child Neurology · 2002
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsMcMaster University Medical CentreSickKids FoundationHospital for Sick ChildrenStatistics CanadaUniversity of Toronto
FundersChildren's Hospital Foundation
KeywordsMedicineSpinal muscular atrophyBicepsSMA*Deltoid curveDeltoid musclePhysical medicine and rehabilitationPhysical therapyMuscle atrophyRandomized controlled trialIsometric exerciseStimulationAtrophyInternal medicineSurgery

Abstract

fetched live from OpenAlex

The study aimed to evaluate the effect of low‐intensity nighttime therapeutic electrical stimulation (TES) on arm strength and function in children with intermediate type spinal muscular atrophy (SMA). The design was a randomized controlled trial with a 6‐month baseline control period. Children were evaluated at baseline, 6, and 12 months. TES was applied from 6 to 12 months to the deltoid and biceps muscle, of a randomly selected arm with the opposite arm receiving a placebo stimulator. Thirteen individuals with SMA between 5 to 19 years of age were recruited into the study and eight completed the 12‐month assessment. No statistically significant differences between the treatment and control arm were found at baseline, 6, and 12 months for elbow flexors, or shoulder abductors on quantitative myometry or manual muscle testing. There was no significant change in excitable muscle mass assessed by M‐wave amplitudes, nor function on the Pediatric Evaluation of Disability Inventory (self‐care domain). Therefore, in this study there was no evidence that TES improved strength in children with SMA.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.531

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 head0.020
GPT teacher head0.276
Teacher spread0.256 · 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

Citations20
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueDevelopmental Medicine & Child NeurologySame topicNeurogenetic and Muscular Disorders ResearchFrench-language works237,207