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Record W4210783579 · doi:10.1139/facets-2021-0111

Lower extremity muscle strength reference values for young Saudi male adults aged 21 to 23 years and their correlation with anthropometric parameters and balance

2022· article· en· W4210783579 on OpenAlexvenueno aff
Jaya Shanker Tedla, Devika Rani Sangadala, Ravi Shankar Reddy, Kumar Gular, Venkata Nagaraj Kakaraparthi, Paul Silvian Samuel

Bibliographic record

VenueFACETS · 2022
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsnot available
FundersKing Khalid University
KeywordsAnthropometryBalance (ability)Muscle strengthAnkleBody mass indexMedicinePhysical medicine and rehabilitationPhysical therapyReference valuesPopulationAnatomyInternal medicine

Abstract

fetched live from OpenAlex

Normative reference values for muscle strength are needed for muscle strength assessment and estimation of prognosis in lower extremity related neuromuscular disorders, as muscle strength influenced by anthropometric parameters and muscle strength itself affects balance. Normative reference values for muscle strength in the Saudi Arabian population is lacking in the literature. Hence, the current study aimed to establish normative reference values for lower extremity muscle strength and to correlate these values with anthropometric parameters and balance. Lower extremity muscle strength and balance were assessed by baseline hand-held dynamometer and by forward, lateral, and oblique direction reach tests in 421 young male adults between 21 and 23 years of age. The mean and standard deviation of lower extremity strength ranged from 43.83 ± 16.92 lb to 62.07 ± 10.74 lb. Body weight, body mass index, and oblique and lateral reach distances were correlated with some knee and ankle muscle strength values. Body weight, body mass index, and balance showed significant effect on lower extremity muscle strength. Furthermore, decreased muscle strength may impair balance; the current clinical practice should evaluate body weight, body mass index, and muscle strength during the assessment process.

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 score0.093
Threshold uncertainty score0.393

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 head0.018
GPT teacher head0.252
Teacher spread0.234 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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