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Record W2586513919 · doi:10.1123/jab.2016-0061

Influence of a Full-Body Compression Suit on Trunk Positioning and Knee Joint Mechanics During Lateral Movements

2017· article· en· W2586513919 on OpenAlexaff
Guillaume Mornieux, Elmar Weltin, Monika Pauls, Franz Rott, Albert Gollhofer

Bibliographic record

VenueJournal of Applied Biomechanics · 2017
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsTrunkKinematicsKnee JointJoint (building)Rotation (mathematics)Compression (physics)OrthodonticsMedicineGeologyMathematicsPhysicsStructural engineeringSurgeryEngineeringGeometryBiology

Abstract

fetched live from OpenAlex

Trunk positioning has been shown to be associated with knee joint loading during athletic tasks, especially changes of direction. The purpose of the present study was to test whether a full-body compression suit (FBCS) would improve trunk positioning and knee joint control during lateral movements. Twelve female athletes performed lateral reactive jumps (LRJ) and unanticipated cuttings with and without the customized FBCS, while 3D kinematics and kinetics were measured. FBCS did not influence trunk positioning during LRJ and led to increased trunk lateral lean during cuttings (P < .001). However, while wearing FBCS, knee joint abduction and internal rotation angles were reduced during LRJ (P < .001 and P = .013, respectively), whereas knee joint moments were comparable during cuttings. FBCS cannot support the trunk segment during unanticipated dynamic movements. But, increased trunk lateral lean during cutting maneuvers was not high enough to elicit increased knee joint moments. On the contrary, knee joint abduction and internal rotation were reduced during LRJ, speaking for a better knee joint alignment with FBCS. Athletes seeking to improve trunk positioning may not benefit from a FBCS.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.452

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 head0.009
GPT teacher head0.260
Teacher spread0.250 · 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 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

Citations3
Published2017
Admission routes1
Has abstractyes

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