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Record W4402257266 · doi:10.1113/ep091743

Joint kinematic responses of Olympic medallist skiers to repeated slalom runs

2024· article· en· W4402257266 on OpenAlexaff
Marine Alhammoud, Robin Trama, Christophe Hautier, Olivier Girard, Sébastien Racinais, Clint Hansen, Frédéric Meyer, Abdulaziz Farooq, Jérémy Coint, Thibaut Trameau, Loïc Brun, Baptiste Morel

Bibliographic record

VenueExperimental Physiology · 2024
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsKinematicsMathematicsKnee JointKnee flexionHip flexionLower limbPhysical medicine and rehabilitationJoint (building)AthletesRepetition (rhetorical device)OrthodonticsMedicinePhysical therapySimulationComputer scienceRange of motionPhysicsSurgeryStructural engineeringEngineering

Abstract

fetched live from OpenAlex

This case study aims to examine changes in the lower limb joint kinematic profile and performance stability induced by repeated ski runs in two world-class alpine skiers. Two Olympic medallist alpine skiers were tested during their slalom training, with continuous recording of right knee and hip angles, along with turn time and run time. The eight runs of the training session were analysed with linear mixed models. Results showed no effect of runs repetition on performance (i.e., run and turn time; P ≥ 0.279). There was no global effect of runs repetition on minimal and maximal angles for either the knee or the hip (P > 0.151). There was an interaction between run and leg for the maximal angle of both the knee and hip (P ≤ 0.047), which increased across runs for the outside leg and decreased for the inside leg. The maximal angular velocity for both the knee and hip increased with runs repetition in extension (P ≤ 0.028). There were no overall changes in maximal angular velocity in flexion with runs repetition (P ≥ 0.264), but there was an interaction between run and leg for the knee (P < 0.001) due to faster eccentric velocities across runs for the outside leg and slower velocities for the inside leg. In conclusion, the observed joint kinematic alterations without concomitant performance impairment support the concept of multiple movement strategies in athletes to achieve similar performance, especially under fatigue conditions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score1.000

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)0.0010.000

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 head0.022
GPT teacher head0.328
Teacher spread0.307 · 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.

Study designBench or experimental
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

Citations1
Published2024
Admission routes1
Has abstractyes

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