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Record W2950823755 · doi:10.1080/13813455.2019.1628066

Cardiopulmonary fitness but not muscular fitness associated with visceral adipose tissue mass

2019· article· en· W2950823755 on OpenAlexaff
Ki‐Yong An, Sue Kim, Minsuk Oh, Hye Sun Lee, Hyuk In Yang, Hyuna Park, Ji‐Won Lee, Justin Y. Jeon

Bibliographic record

VenueArchives of Physiology and Biochemistry · 2019
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Alberta
FundersH2020 LEIT Information and Communication TechnologiesYonsei University College of MedicineNational Research Foundation of Korea
KeywordsMedicinePhysical fitnessOverweightVO2 maxAdipose tissueCardiopulmonary bypassPhysical therapyObesityCardiologyInternal medicineHeart rateBlood pressure

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study is to identify the association between physical fitness (cardiopulmonary and musculoskeletal) and the distribution of abdominal fat in overweight and obese adults. METHODS: Of the total 102 overweight and obese participants, 99 participants completed all measurements. Cardiopulmonary fitness was assessed by maximal oxygen consumption test and muscular fitness was assessed using 10 repetition max. Visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were assessed by computed tomography (CT) at the level of lumbar spine 4-5. RESULTS: Cardiopulmonary fitness was inversely associated with both VAT and SAT, while muscular fitness only inversely associated with SAT. Multiple linear regression analyses indicated that gender, age, and cardiopulmonary fitness, but not muscular fitness, were associated with VAT, and age, cardiopulmonary fitness, and muscular fitness were significantly associated with SAT. CONCLUSIONS: Cardiopulmonary fitness is more closely related to both VAT and SAT while muscular fitness is related with SAT.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.260
Teacher spread0.251 · 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 designBench or experimental
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

Citations5
Published2019
Admission routes1
Has abstractyes

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