Appetite sensations as a marker of overall intake
Bibliographic record
Abstract
The aim of this study was to evaluate the clinical utility of appetite sensations to characterize individual overall energy intake. A group of men (n 28) and women (n 23) was recruited to record their 'desire to eat', 'hunger', 'fullness' and 'prospective food consumption' (PFC) on visual analogue scales before a standardized meal test, immediately after and every 10 min for a period of 1 h after the meal. The 1 h post-meal area under the curve (1 h AUC) and the satiety quotient (SQ) were calculated for all appetite sensations. In a second visit, all participants were invited to eat three meals in order to measure total energy intake (TEI) and food preferences. Metabolic rate (MR) was also assessed to derive daily relative energy intake (REI) by subtracting this variable from TEI (TEI-MR=REI). The Three-Factor Eating Questionnaire scores were also calculated for all participants. One h AUC for fullness was the appetite sensation most strongly associated with TEI and REI (r-0.42, P< or =0.003 and r-0.32, P< or =0.05, respectively). SQ for fullness was the only predictor of TEI and REI (r-0.42, P< or =0.0003 and r-0.30, P< or =0.05, respectively). Restraint, disinhibition and hunger scores were not associated with appetite sensation variables. These results suggest that the fullness dimension seems to be a useful appetite sensation to predict long-term TEI and REI. Thus, assessment of appetite sensation such as fullness in response to a fixed load may be useful to evaluate individual overall energy intake.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".