A systematic review of the use of the Satiety Quotient
Bibliographic record
Abstract
The satiating efficiency of food has been increasingly quantified using the Satiety Quotient (SQ). The SQ integrates both the energy content of food ingested during a meal and the associated change in appetite sensations. This systematic review examines the available evidence regarding its methodological use and clinical utility. A literature search was conducted in six databases considering studies from 1900 to April 2020 that used SQ in adults, adolescents and children. All study designs were included. From the initial 495 references found, fifty-two were included. Of the studies included, thirty-three were acute studies (twenty-nine in adults and four in adolescents) and nineteen were longitudinal studies in adults. A high methodological heterogeneity in the application of the SQ was observed between studies. Five main utilisations of the SQ were identified: its association with (i) energy intake; (ii) anthropometric variables; (iii) energy expenditure/physical activity; (iv) sleep quality and quantity and (v) to classify individuals by their satiety responsiveness (i.e. low and high satiety phenotypes). Altogether, the studies suggest the SQ as an interesting clinical tool regarding the satiety responsiveness to a meal and its changes in responses to weight loss in adults. The SQ might be a reliable clinical indicator in adults when it comes to both obesity prevention and treatment. There is a need for more standardised use of the SQ in addition to further studies to investigate its validity in different contexts and populations, especially among children and adolescents.
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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 | 0.018 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".