Dietary pulses, satiety and food intake: A systematic review and meta‐analysis of acute feeding trials
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of dietary pulses (beans, peas, chickpeas, lentils) on acute satiety and second meal intake, a systematic review and meta-analysis was conducted. METHODS: MEDLINE, EMBASE, CINAHL, and the Cochrane Registry (through May 6, 2013) were searched for acute controlled trials examining the effect of dietary pulses on postprandial satiety or second meal intake compared with isocaloric controls. Two independent reviewers extracted data and assessed methodological quality and risk of bias. Data were pooled by generic inverse variance random effects models and expressed as ratio of means (RoMs) for satiety and mean differences (MDs) for second meal food intake, with 95% confidence intervals (95% CIs). Heterogeneity was assessed (Q statistic) and quantified (I(2) statistic). Protocol registration: clinicaltrials.gov identifier, NCT01605422. RESULTS: Nine trials met the eligibility criteria. Dietary pulses produced a 31% greater satiety incremental area under the curve (IAUC) (RoM = 1.31, 95% CI: 1.09 to 1.58, P = 0.004; Phet = 0.96; I(2) = 0%) without affecting second meal intake (MD = -19.94, 95% CI: -75-35, P = 0.48; Phet = 0.01; I(2) = 63%). Our data are limited by the small sample sizes, narrow participant characteristics and significant unexplained heterogeneity among the available trials. CONCLUSIONS: Pooled analyses show that dietary pulses contribute to acute satiety but not second meal 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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.027 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".