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Record W2106834530 · doi:10.1159/000435840

Effect of an Acute High Carbohydrate Diet on Body Composition Using DXA in Young Men

2015· article· en· W2106834530 on OpenAlexaff
Marc-Antoine Rouillier, Sarah David-Riel, Anne‐Sophie Brazeau, David H. St‐Pierre, Antony D. Karelis

Bibliographic record

VenueAnnals of Nutrition and Metabolism · 2015
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsMcGill UniversityUniversité du Québec à Montréal
Fundersnot available
KeywordsCarbohydrateCalorieLean body massAnimal scienceBody mass indexInternal medicineObesityBody weightComposition (language)EndocrinologyBody waterChemistryMedicineBiology

Abstract

fetched live from OpenAlex

AIM: The aim of this study is to investigate the effect of a 3-day high-carbohydrate diet (≥75% of total calories) on body composition using dual-energy X-ray absorptiometry (DXA). METHODS: Twenty non-obese young men (age 22.7 ± 2.6 years, BMI 23.5 ± 2.1 kg/m(2)) completed the study. Two DXA tests were performed for the measurement of total body weight, body mass index (BMI), body fat percentage as well as total, appendicular and central lean body mass (LBM) before and after a high-carbohydrate diet for 3 days. In addition, the participants completed a food diary during the 3-day high-carbohydrate diet to determine the mean percentage of carbohydrates consumed from total kilocalories. RESULTS: The mean percentage of carbohydrate intake over 3 days was 83.7 ± 8.4%. Our results showed a significant increase in total body weight, BMI as well as total and appendicular LBM after the high-carbohydrate diet (p < 0.01). In addition, we observed a strong tendency for lower body fat percentage values after the intervention (p = 0.05). No significant difference was observed for central LBM. CONCLUSIONS: These results indicate that the effect of an acute high carbohydrate diet seems to affect body composition values using DXA, such as total LBM. This study may lead to the need of standardizing a diet prior to using DXA.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.042
GPT teacher head0.350
Teacher spread0.307 · 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

Citations30
Published2015
Admission routes1
Has abstractyes

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