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Record W4402891097 · doi:10.1186/s13104-024-06944-z

Side differences in upper quarter mobility/stability are not related to serve velocity in tennis players with different levels of training experience

2024· article· en· W4402891097 on OpenAlexaboutno aff
Johanna Lambrich, Stefan Panzer, Thomas Muehlbauer

Bibliographic record

VenueBMC Research Notes · 2024
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
FundersUniversität Duisburg-EssenDeutsche Forschungsgemeinschaft
KeywordsQuarter (Canadian coin)MedicineStroke (engine)Balance (ability)Physical medicine and rehabilitationPhysical therapyPhysics

Abstract

fetched live from OpenAlex

OBJECTIVE: Tennis is characterised by repetitive serves and strokes predominately performed with one arm. This can lead to differences in upper quarter mobility/stability between the stroke and non-stroke arm, which could even enlarge with increasing training experience and negatively affect serve velocity. Thus, we determined side differences (i.e., limb symmetry index) in upper quarter mobility/stability and their association with flat and slice serve velocity in advanced (ITN ≤ 4) female and male tennis players (N = 42, mean age = 23.9 ± 9.3 years) with different levels of training experience (< 2 years: n = 14, 2-5 years: n = 17, 6-8 years: n = 11). RESULTS: Y Balance Test-Upper Quarter (YBT-UQ) side difference (i.e., composite score) and performances (i.e., medial reach) were largest in players with the lowest level of training experience (i.e., < 2 years). Further, YBT-UQ performances (i.e., medial reach and composite score) but not side differences were significantly correlated with flat and slice serve velocity, particularly in less experienced players. Our results suggest that significant side differences in upper quarter mobility/stability occur in less experienced players (indicative of increased injury risk) but they are not related to tennis-specific performance (i.e., serve velocity).

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)0.002
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 score0.025
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.284
GPT teacher head0.414
Teacher spread0.131 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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