MétaCan
Menu
Back to cohort
Record W2766589894 · doi:10.5430/jbar.v6n2p50

Kinematics of Shoulder Injuries in Throwing Sports

2017· article· en· W2766589894 on OpenAlexvenueno aff
Hongqiong Deng, Yuan Li, Hong Xie, Shiwei Li

Bibliographic record

VenueJournal of Business Administration Research · 2017
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsThrowingJavelinKinematicsPhysical medicine and rehabilitationBiomechanicsSimulationPsychologyComputer scienceEngineeringMedicineAeronauticsAnatomyPhysics

Abstract

fetched live from OpenAlex

Muscle injury mechanism should be studied to know how to prevent the muscle injuries. The purpose of this article is to construct a protecting model of shoulder injuries based on classical mechanism and kinematics in throwing sports like baseball pitching, badminton smash, volleyball smash, javelin throwing process etc, and then introduce the products design. Firstly, the biomechanics of muscle were analyzed based on Newton's classical mechanics; then a finite element analysis was used to simulate the shoulder muscles protection. Protective parameters were got to make the protective clothes; finally, the protective performance and the comfortableness has been evaluated by ten healthy participants with the mean age, height, body mass were 23.5 years (SD, 1.5), 1.75m (SD, 0.07), 60.5 kg (SD, 9.1), respectively. The throwing test including the baseball pitching, the badminton smash, the process of volleyball smash, and the javelin throwing process. Three protective clothes have been produced and all of them have a protective effect on the muscle while participants were doing the baseball pitching motion, the badminton smashes motion and the volleyball smash. And it has also met the requirement of the human body. But protecting effect each style given was different in different sports motions.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.034
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.162
GPT teacher head0.481
Teacher spread0.320 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Business Administration ResearchSame topicShoulder Injury and TreatmentFrench-language works237,207