Effectiveness of legume consumption for facilitating weight loss: a randomized trial
Bibliographic record
Abstract
Legumes are rich in factors which help promote satiety, including fiber, resistant starch and protein. Observational and multi‐meal experimental studies suggest that consuming legumes may help support healthy weight; however, randomized trials are lacking. We are conducting a 6‐wk randomized trial with 6‐wk follow‐up to test whether consuming legumes facilitates weight loss and improves chronic disease risk. After baseline measurements, subjects are advised to reduce daily energy intake (EI) by 30%. About half of the target EI is provided and consumption required, while the remainder of EI is self‐selected. Subjects are randomly assigned to 1 of 3 groups: DG: the US Dietary Guideline recommendation of 3 c/wk of legumes (0.5 c/d, 6 d/wk); DRI: the amount of legumes needed to meet the Dietary Reference Intake for fiber (women 1.8 c/d, men 2.7 c/d, 6 d/wk); or control: minimal legumes. To date, 28 subjects have enrolled (goal n=66). Mean±SEM weight loss thus far was: wk 3 (−1.7±0.3 kg), wk 6 (−2.7±0.7 kg), and wk 12 (−2.5±0.8 kg) (p 0.01 for each)). At wk 3, weight loss was greater in the DG and DRI groups (−2.3±0.4 kg; −2.4±0.7 kg) vs the control group (−0.9±0.4 kg)(n=19; p=0.046). At wk 6, weight loss also differed among groups (DG: −3.4±0.9 kg; DRI: −3.9±1.4 kg; control: 1.0±1.1 kg (n=15; p=0.11–0.12 control vs DG/DRI). Consuming at least 0.5 c/d of legumes may assist with weight loss and maintenance. [Funding: Pulse Canada]
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".