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Record W1806623434 · doi:10.1155/2015/140139

Gender Differences in the Appetite Response to a Satiating Diet

2015· article· en· W1806623434 on OpenAlexafffund
Alexandra Bédard, Anne-Marie Hudon, Vicky Drapeau, Louise Corneau, Sylvie Dodin, Simone Lemieux

Bibliographic record

VenueJournal of Obesity · 2015
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health ResearchHeart And Stroke Foundation Of QuebecHeart and Stroke Foundation of Canada
KeywordsMealMedicineAlgorithmAppetiteAnimal scienceInternal medicineComputer scienceBiology

Abstract

fetched live from OpenAlex

We examined gender differences in appetite sensations when exposed to Mediterranean diet (MedDiet) meals and determined whether there are gender differences in the change in the satiating properties of the MedDiet over time. Thirty-eight men and 32 premenopausal women consumed a 4-week isoenergetic MedDiet under controlled conditions. Visual analogue scales were used to measure perceived appetite sensations before and immediately after each meal consumed over the course of one day (Wednesday) of the first and the fourth week of intervention. Women reported greater decreases for desire to eat, hunger, and appetite score than men in response to the consumption of the MedDiet meals (gender-by-meal interactions, resp., P = 0.04, P = 0.048, and P = 0.03). Fullness and prospective food consumption responses did not significantly differ between men and women. Between the first and the fourth week of intervention, premeal prospective food consumption increased with time in men (P = 0.0007) but not in women (P = 0.84; P for gender-by-time interaction = 0.04). These results indicate gender differences in appetite sensations when exposed to the MedDiet. These results may be useful in order to have a better understanding of gender issues for body weight management.

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)0.001
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.090
GPT teacher head0.350
Teacher spread0.260 · 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

Citations46
Published2015
Admission routes2
Has abstractyes

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