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Record W3180605183

Relationship between core endurance and dynamic balance in professional basketball players: A pilot study

2021· article· en· W3180605183 on OpenAlexaboutno aff
Abhilash Pv, Sudeep Shetty, K Anjana

Bibliographic record

VenueInternational Journal of Physical Education Sports and Health · 2021
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsBasketballDynamic balanceCore (optical fiber)Balance (ability)Test (biology)Balance testPhysical therapyPhysical medicine and rehabilitationPsychologyMedicineEngineeringMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

Background: The Core strength is an important precondition for basketball players. The good balance in basketball sports provides a controlled body for players, minimizes their errors, quick movers, and effective technical skills. The study was to determine the relationship between core endurance and dynamic balance in Professional basketball players. Methodology: A pilot study was conducted on 20 professional basketball players between 18 to 28 years of age. Core endurance was assessed using the McGill core endurance test and dynamic balance was assessed using the star excursion balance test.Results: Karl Pearson’s correlation coefficient test was used to find the relation between the core endurance and dynamic balance. There was a positive correlation between core endurance and dynamic balance of bilateral leg (r = .471, P

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 score0.011
Threshold uncertainty score0.279

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 head0.088
GPT teacher head0.453
Teacher spread0.364 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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