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Record W4414015842 · doi:10.11159/icbes25.121

Upper Limb Injuries and Biomechanical Insights Among Recreational Padel Players in Kuwait

2025· article· en· W4414015842 on OpenAlexvenueno aff
Mahdi Yousef Farhat, Lujain Moussa, Behzad Bashiri

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2025
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsnot available
Fundersnot available
KeywordsRecreationBiomechanicsUpper limbComputer sciencePhysical medicine and rehabilitationMedicineAnatomyBiology

Abstract

fetched live from OpenAlex

Injuries are a common consequence of sports that can significantly impact players' quality of life, especially among recreational players.Padel, a sport popular in Kuwait, exposes players to the risk of musculoskeletal injuries mainly in the upper limbs.Understanding the biomechanical aspects of this sport and how to prevent injuries are important for the well-being of recreational players.This pilot study aimed to evaluate a methodology for gathering insights into the prevalence of upper limb injuries among recreational padel players, and the strategies they use to reduce injury risk.A questionnaire was distributed to recreational padel players, to assess injury types, frequency, and preventive measures taken by players.The results showed that injuries were prevalent in the shoulder and wrist, closely followed by the elbow and hand.Among the prevention strategies, the players indicated upper limb strength training as an important measure to reduce injury risks.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.224
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), 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
Published2025
Admission routes1
Has abstractyes

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