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Record W2025458407 · doi:10.1079/bjn2000224

Combined effects of red pepper and caffeine consumption on 24 h energy balance in subjects given free access to foods

2001· article· en· W2025458407 on OpenAlexaff
Mayumi Yoshioka, Éric Doucet, Vicky Drapeau, Isabelle J. Dionne, Angelo Tremblay

Bibliographic record

VenueBritish Journal Of Nutrition · 2001
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPepperCaffeineFood scienceAnimal scienceIngestionMedicineToxicologyChemistryInternal medicineBiology

Abstract

fetched live from OpenAlex

The effects of red pepper and caffeine ingestion on energy and macronutrient balances were examined in eight Caucasian male subjects. All subjects participated in two randomly assigned conditions: control and experimental (red pepper and caffeine). After ingesting a standardized breakfast, subjects ate three meals ad libitum (lunch, dinner and breakfast) and snacks which were served approximately 2 h after the lunch and dinner over a 24 h period. Two appetizers with or without 3 g red pepper) were given before lunch and dinner, and a drink (decaffeinated coffee with or without 200 mg caffeine) was served at all meals and snacks except for the after-dinner snack. It is also important to note that on the experimental day, 8.6 and 7.2 g red pepper were also added to lunch and dinner respectively. Red pepper and caffeine consumption significantly reduced the cumulative ad libitum energy intake and increased energy expenditure. The mean difference in energy balance between both conditions was 4000 kJ/d. Moreover, the power spectral analysis of heart rate suggested that this effect of red pepper was associated with an increase in sympathetic:parasympathetic nervous system activity ratio. These results indicate that the consumption of red pepper and caffeine can induce a considerable change in energy balance when individuals are given free access to foods.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.449

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 head0.020
GPT teacher head0.297
Teacher spread0.277 · 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

Citations138
Published2001
Admission routes1
Has abstractyes

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