Unveiling the Evidence for the Use of Pulses in Managing Type 2 Diabetes Mellitus: A Scoping Review
Bibliographic record
Abstract
Management of type 2 diabetes mellitus (T2DM) is a pressing global healthcare challenge. Innovative strategies that integrate superior medical and nutritional practices are essential for holistic care. As such, pulse consumption is encouraged for its potential benefit in reducing hypercholesterolaemia, dyslipidaemia, and triglyceride levels, as well as enhancing glycaemic control. This scoping review aims to assess the depth of evidence supporting the recommendation for pulse consumption in T2DM management and to identify gaps in the existing literature. We conducted a comprehensive search across the databases MEDLINE, Global Health, EMBASE, CINAHL, Web of Science, and the Cochrane Library (up to July 2023). We included population-based studies of any design, and excluded review-style articles. Articles published in languages other than English were also excluded. From the 2449 studies initially identified, 28 met our inclusion criteria. Acute postprandial trials demonstrated improved glucose responses and enhanced insulin responses to pulse-based intervention. Meanwhile, long-term trials reported meaningful improvements in T2DM indicators such as haemoglobin A1C (HbA1c), fasting glucose, fasting insulin, C-peptide, and markers of insulin resistance like homeostatic model assessment (HOMA). Integrating more pulses into the diets of diabetic individuals might offer an efficient and cost-effective strategy in the global initiative to combat T2DM.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".