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Record W1493094985 · doi:10.3233/ies-2011-0406

Hamstring to quadriceps strength ratio and noncontact leg injuries: A prospective study during one season

2011· article· en· W1493094985 on OpenAlexaff
Daehan Kim, Junggi Hong

Bibliographic record

VenueIsokinetics and Exercise Science · 2011
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsHamstringLeg muscleMedicineHamstring musclesProspective cohort studyPhysical medicine and rehabilitationPhysical therapySurgery

Abstract

fetched live from OpenAlex

Previous studies have proposed that thigh muscle imbalance is a critical risk factor for the athletic non-contact knee injuries. However, there is a little consensus among prospective studies with regard to the correlation between isokinetic hamstring to quadriceps strength ratio (HQR) and the non-contact knee injury rates. Most of athletic movements at risk are closed kinetic chain movements, and compensatory effect among ankle, knee, and hip joints during the closed kinetic chain movement was observed in the previous literatures. Therefore, it is assumed that hamstrings and quadriceps (H:Q) imbalance can cause non-contact lower extremity injuries without necessarily causing knee injuries. The purpose of this study was to prospectively investigate the relationship between H:Q strength imbalance and overall non-contact lower extremity injuries. A prospective cohort study was conducted on NCAA division III basketball and soccer players during one season. A total of eighty two NCAA Division III athletes (41 female [19.56 ± 1.34 yrs, 68.2 ± 10.84 kg, 166.3 ± 6.78 cm] and 40 male [19.97 ± 1.43 yrs, 75.45± 8.23 kg, 173.21 ± 7.65 cm]) volunteered to participate in this study which tested Q and H strength at 60°/s. A trend (p< 0.05) indicating that lower than 60% of HQR was associated with non-contact leg injuries was apparent. This suggests that the H:Q imbalance may be of significance in athletic non-contact leg injuries.

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 score0.097
Threshold uncertainty score0.504

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 head0.015
GPT teacher head0.266
Teacher spread0.251 · 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

Citations66
Published2011
Admission routes1
Has abstractyes

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