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Record W3006456291

The effect of a six-week plyometric training on dynamic balance and knee proprioception in female badminton players.

2019· article· en· W3006456291 on OpenAlexaff
Raana Alikhani, Shahnaz Shahrjerdi, Masod Golpaigany, Mohsen Kazemi

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsCanadian Memorial Chiropractic College
Fundersnot available
KeywordsProprioceptionAnterior cruciate ligamentPlyometricsBalance (ability)MedicineDynamic balancePhysical therapyAnterior Cruciate Ligament InjuriesSignificant differenceACL injuryPhysical medicine and rehabilitationSurgeryInternal medicinePhysics
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: Non-contact anterior cruciate ligament (ACL) injury is one of the most common severe injuries among female badminton players. Dynamic balance (DB) and knee proprioception (KP) are critical in preventing this injury. The purpose of this study was to investigate the effect of a six-week plyometric training (PT) program on DB and KP in female badminton players. METHODS: Twenty-two healthy beginner female badminton players were randomly assigned to either control (CG) or experimental group (ExG). The ExG went through PT for six weeks. Pre- and post-intervention Y balance and photography tests were used to assess DB and KP, respectively. RESULTS: There was no difference between groups prior to PT in DB (p=0.804) and KP (at 45°, p=0.085 and at 60°, p=0.472 angles; p>0.05). However, after the PT only ExG improved significantly in DB (p=0.003) and KP (at 45°, p=0.004 and at 60°, p=0.010 angles; p<0.05). CONCLUSION: Female badminton players' dynamic balance and knee proprioception improved significantly after plyometric training (PT). These results may be important in preventing non-contact anterior cruciate ligament (ACL) injury, which requires further investigation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score0.222

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.240
Teacher spread0.230 · 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

Citations38
Published2019
Admission routes1
Has abstractyes

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