Effect of non‐oil seed pulses on glycemic control: a meta‐analysis of randomized controlled experimental trials in humans.
Bibliographic record
Abstract
Background Dietary pulses (beans, lentils, chickpeas, etc.) are a good source of viscous fibre and a valuable means for lowering the glycemic‐index (GI) of the diet. Objective To assess the effect of pulses on glycemic control, we conducted a meta‐analysis of experimental trials investigating the effect of pulses alone or in low‐GI or high‐fibre diets on indices of glycemic control. Methods We searched MEDLINE, EMBASE, CINAHL, and Cochrane Library for relevant controlled trials of =7d in humans. Two independent reviewers extracted information on study design, participants, treatments, and outcomes. Generic inverse variance models were used for pooled analyses. Heterogeneity was assessed by Chi 2 and quantified by I 2 . Results Forty‐four trials were included. Pulses alone (12 trials) lowered fasting blood glucose (FBG) (standardized mean difference [SMD] ‐0.81 [95% CI ‐1.32,‐0.29]) and insulin (‐0.61[‐1.07,‐0.14]). Pulses in low‐GI‐diets (19 trials) lowered glycosylated proteins (GPs) (‐0.28[‐0.42, ‐0.14]). Pulses in high‐fibre diets (12 trials) lowered FBG (‐0.22[‐0.43,‐0.00]) and GPs (‐0.33[‐0.59,‐0.06]). Inter‐study heterogeneity was significant in all cases. Conclusions These data demonstrate that pulses alone or in low‐GI or high‐fibre diets improve markers of glycemic control in humans. Subgroup analyses are planned to explore sources of heterogeneity. Funding source 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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".