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Record W2810835617 · doi:10.12965/jer.1836148.074

Determination of the relationship between core endurance and isokinetic muscle strength of elite athletes

2018· article· en· W2810835617 on OpenAlexaboutno aff
Tuğba Kocahan, Bihter Akınoğlu

Bibliographic record

VenueJournal of Exercise Rehabilitation · 2018
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsnot available
Fundersnot available
KeywordsCore (optical fiber)AthletesMedicinePhysical therapyShouldersExternal rotationPhysical medicine and rehabilitationCore strengthCore stabilityMuscle strengthInternal rotationMaterials scienceSurgery

Abstract

fetched live from OpenAlex

Muscle strength and core endurance are both factors contributing to athletes' performance and prevalence of injuries. There are no studies indicating the relationship between muscle strength around the shoulder and knee joints and core endurance. The aim of our study is to determine the relationship between core endurance and isokinetic muscle strength of knees and shoulders of elite athletes. Seventy-one elite athletes (weight lifting, boxing, taekwondo, biathlon, and ice skating) (age, 18.13±2.9 years) were included in the study. Isokinetic muscle strength of shoulder internal-external rotation and knee flexion-extension were determined by using an Isomed 2000 device. Core endurance of athletes was assessed using the Mcgill Core Endurance Tests. There was a relationship between the shoulder internal rotation and external rotation peak torque/body weight (PT/W) and all endurance tests except extension endurance tests. There was also a relationship between knee flexion PT/W and all core endurance tests. While there was a relationship between knee extension PT/W and extension endurance and the lateral bridge test, this relationship was not found with the flexor endurance test. These results indicate that the upper and lower extremity muscle strength and core endurance of athletes are related with each other and must be evaluated and trained as a whole with each other.

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)0.001
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 score0.107
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.023
GPT teacher head0.308
Teacher spread0.285 · 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

Citations26
Published2018
Admission routes1
Has abstractyes

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