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Record W3132116899 · doi:10.1080/15438627.2021.1888098

Performing more than 20 purposeful gameplay headers in a soccer season may alter autonomic function in female youth soccer players

2021· article· en· W3132116899 on OpenAlexaff
Marquise M. Bonn, Alexandra Harriss, James W.G. Thompson, James P. Dickey

Bibliographic record

VenueResearch in Sports Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsWestern University
Fundersnot available
KeywordsHeart rate variabilityAutonomic functionAutonomic nervous systemHeart rateMedicinePhysical therapyPsychologyAnimal scienceInternal medicineBiologyBlood pressure

Abstract

fetched live from OpenAlex

This study evaluated the effects of cumulative purposeful soccer heading on autonomic nervous system function in 22 female youth soccer players (13.3 ± 0.9 years). A 10 minute electrocardiogram recording was collected at baseline and following the 20 game season (post-season) to calculate measures of heart rate variability (HRV), including standard deviation of the normal-normal intervals, total power, high frequency (HF), low frequency (LF), LF:HF, normalized HF and normalized LF. Participants were categorized into low- (<20 headers per season; n = 13) and high- (>20 headers per season; n = 9) exposure groups. Mann–Whitney U tests demonstrated no significant differences between groups for any HRV metric. However, the increased normalized LF power (low exposure 8.67 and high exposure −31.17, respectively; r = 0.35) and LF:HF power (−6.39 and 15.80, respectively; r = 0.35), between groups had moderate practical significance. Therefore, female youth players who perform more than 20 purposeful headers during a soccer season may exhibit altered autonomic function.

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)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.088
GPT teacher head0.373
Teacher spread0.285 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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