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Record W3025609454 · doi:10.1002/tsm2.172

Association between lower extremity muscular strength and acute knee injuries in young team‐sport athletes

2020· article· en· W3025609454 on OpenAlexaff
Jussi Hietamo, Jari Parkkari, Mari Leppänen, Kathrin Steffen, Pekka Kannus, Tommi Vasankari, Ari Heinonen, Ville M. Mattila, Kati Pasanen

Bibliographic record

VenueTranslational Sports Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsAlberta Children's HospitalAlberta Bone and Joint Health InstituteUniversity of Calgary
FundersKementerian Pendidikan dan Kebudayaan
KeywordsMedicineIsometric exerciseAthletesBasketballPhysical therapyPhysical strengthACL injuryPhysical medicine and rehabilitationAnterior cruciate ligamentSurgery

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate LE muscular strength variables as potential risk factors for all and non-contact acute knee and ACL injuries in young athletes. A total of 188 young (≤21) male and 174 female basketball and floorball players participated in LE muscular strength tests and were followed up to 3 years. The strength test battery consisted of 1RM leg press, maximal concentric isokinetic (60°/s) quadriceps and hamstrings, and maximal isometric hip abductor strength. The outcomes were a new acute knee or ACL injury and a new acute non-contact knee or ACL injury. A total of 51 (17 in males and 34 in females) new acute knee injuries registered and 17 (one in males and 16 in females) of these were ACL injuries. In the adjusted Cox regression models, only lower maximal hip abduction strength (kg/kg) was significantly associated with an increased risk of all knee injuries in males (HR 1.80 [95% CI, 1.03-3.16] for 1 SD decrease in hip abduction). However, ROC curve analysis showed an area under the curve 0.66 revealing that maximal hip abduction strength test cannot be used as a screening tool for an acute knee injury in young male athletes.

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 score0.020
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.009
GPT teacher head0.257
Teacher spread0.248 · 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

Citations24
Published2020
Admission routes1
Has abstractyes

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