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Record W2964442415 · doi:10.3389/fped.2019.00345

Greater Reduction in Abdominal Than in Upper Arms Subcutaneous Fat in 10- to 12-Year-Old Tennis Players: A Volumetric MRI Study

2019· article· en· W2964442415 on OpenAlexaff
Joaquín Sanchis‐Moysi, José Antonio Serrano, Juan José González, José A. L. Calbet, Cecilia Dorado

Bibliographic record

VenueFrontiers in Pediatrics · 2019
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of British Columbia
FundersUniversidad de Las Palmas de Gran CanariaMinisterio de Educación, Cultura y DeporteUniversidad Europea de Madrid
KeywordsMedicineReduction (mathematics)Subcutaneous fatAbdominal fatAnatomySurgeryRadiologyAdipose tissueInternal medicineBody weight

Abstract

fetched live from OpenAlex

Background. Little is known about the impact of long term participation in sports and subcutaneous fat volume in children. This study aimed at determining whether tennis participation is associated with lower subcutaneous adipose tissue volume (SATv) in the abdominal and upper extremities in children. Methods. Magnetic resonance imaging was used to determine the SATv stored in the abdominal region and upper arms in seven tennis players and seven inactive children matched by height and age (147 cm and 10.9 years). All participants were in Tanner stage 1 or 2. Results. Playing tennis was associated with 48% (P = 0.001) lower abdominal SATv and 17-18% (P > 0.05) lower upper arms SATv compared to controls. The ratio between abdominal/upper arms SATv was larger in the controls vs tennis players (69% P = 0.001). The SATv was similar in the dominant and non-dominant arm within each group. Conclusion. Playing tennis during childhood is associated with reduced SATv in the abdominal region and a more favourable regional distribution of fat. Despite the large amount of contractile activity of the playing (dominant) arm, there was no indication of between-arms differences in SATv.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
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 head0.008
GPT teacher head0.242
Teacher spread0.234 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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