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Record W4405228770 · doi:10.1123/ijatt.2024-0049

Association Between Lower Extremity Muscle Strength and Knee Loading During 180° Pivot Turn in Female Players

2024· article· en· W4405228770 on OpenAlexaff
Mari Leppänen, Jari Parkkari, Tron Krosshaug, Tommi Vasankari, Pekka Kannus, Kati Pasanen

Bibliographic record

VenueInternational Journal of Athletic Therapy & Training · 2024
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsAlberta Children's HospitalAlberta Bone and Joint Health InstituteUniversity of Calgary
Fundersnot available
KeywordsAssociation (psychology)Muscle strengthPhysical medicine and rehabilitationTurn (biochemistry)Physical therapyMedicinePsychologyPhysics

Abstract

fetched live from OpenAlex

Sufficient muscle strength is suggested to reduce frontal plane knee loading during change of direction maneuvers. However, it is currently not thoroughly understood if lower extremity strength is associated with increased frontal plane knee biomechanics during change of direction in youth female team sport players. The objective of this cross-sectional study was to investigate the influence of maximal muscle strength on knee valgus angle and knee abduction moment during 180° pivot turn in 106 youth female team sport players. Lower hip abductor strength, lower knee extensor strength, and higher knee flexor strength were associated with increased knee valgus. Higher knee flexor and leg press strength were associated with increased knee abduction moment. The study found associations between both decreased and increased lower extremity muscle strength and frontal plane knee biomechanics. However, these associations could explain only 20% of the variance in frontal plane knee biomechanics at best.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.022
GPT teacher head0.297
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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