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Record W2990076835 · doi:10.14740/jocmr3998

Variation of Salivary IgA During Weight Loss Period Before a Competition Among University Judo Players

2019· article· en· W2990076835 on OpenAlexvenueno aff
Hiroaki Hiraoka, Yukichi Hanaoka, Subrina Jesmin, Fuminori Kimura, Yujiro Matsuish, Kazuhiro Shimizu, Koichi Watanabe

Bibliographic record

VenueJournal of Clinical Medicine Research · 2019
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsnot available
FundersKozuki Foundation
KeywordsWeight lossSalivaMedicineAthletesVitalityPhysiologyInternal medicineBody weightPhysical therapyObesityBiology

Abstract

fetched live from OpenAlex

BACKGROUND: This study aims to investigate the health condition of university judo athletes during a period of weight loss before a competition, using secretory immunoglobulin A (SIgA) in saliva. METHODS: The subjects were 30 university judo athletes, who were divided into three groups: control group (n = 10), under 5% weight loss group (n = 10), and over 5% weight loss group (n = 10). The items evaluated were body weight measurements, salivary SIgA level and its secretion rates, incidences of symptoms of upper respiratory tract infections, and mental state. RESULTS: The over 5% body weight loss group had significantly lower SIgA secretion rates on the first day and third day before the real competition compared to those of the control group. Furthermore, the over 5% body weight loss group had an increased number of symptoms of upper respiratory tract infections. Alterations in mental statuses were also seen, such as decreased vitality and increased fatigue on the day before the competition in the over 5% weight loss group. CONCLUSIONS: This study shows that over 5% body weight loss in judo athletes induces a decrease in the salivary SIgA secretion rate and causes health condition of the body and the mind to deteriorate.

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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.087
GPT teacher head0.441
Teacher spread0.354 · 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.

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

Citations13
Published2019
Admission routes1
Has abstractyes

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