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Record W1971604549 · doi:10.3109/01942638.2011.631102

Relationships Between Lower Limb Muscle Strength and Locomotor Capacity in Children and Adolescents with Cerebral Palsy Who Walk Independently

2011· article· en· W1971604549 on OpenAlexaff
Chantale Ferland, Céline Lepage, Hélène Moffet, Désirée B. Maltais

Bibliographic record

VenuePhysical & Occupational Therapy In Pediatrics · 2011
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversité LavalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsIsometric exerciseAnkleCerebral palsyPhysical medicine and rehabilitationMedicineHeelPhysical therapyPlantar flexionMuscle strengthAnatomy

Abstract

fetched live from OpenAlex

This study aimed to quantify relationships between lower limb muscle strength and locomotor capacity for children and adolescents with cerebral palsy (CP) to identify key muscle groups for strength training. Fifty 6- to 16-year-olds with CP (Gross Motor Function Classification System level I or II) participated. Isometric muscle strength of hip flexor and abductor, knee flexor and extensor, and ankle dorsiflexor muscles was measured using hand-held dynamometry. Ankle plantar flexor concentric muscle strength was assessed as the maximal number of unilateral heel rises. Locomotor capacity was evaluated by the 6-min walk test (6MWT), 10-meter Shuttle Run Test (10mSRT), and Timed Up and Down Stairs Test (TUDS). With control for age, sex, and height, hip flexor and ankle plantar flexor strength explained 47.8% of the variance in the 6MWT and 32.9% of variance in the TUDS and hip abductor isometric strength explained 43.5% of the variance in the 10mSRT. Avenues for future research include randomized controlled trials that specifically target hip flexor muscles, as this has not previously been done, and determining factors other than strength that are likely related to locomotor capacity of children and adolescents with CP.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.695

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 head0.050
GPT teacher head0.271
Teacher spread0.221 · 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

Citations30
Published2011
Admission routes1
Has abstractyes

Explore more

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