Consumption of Pulse-Based Meals Improves Lipoprotein Ratios Among Sedentary Office Workers: A Randomized Clinical Trial
Bibliographic record
Abstract
To determine whether improvements in cardiometabolic health occur when providing pulse-based meals to sedentary office workers. Using a randomized, single-blind, crossover design participants (n = 26) were assigned to either: A pulse-based diet to replace their regular workplace meals OR their regular diet for 2 months, followed by a one-month washout and then crossed-over to the other diet for 2 months. Blood glucose and insulin response (measured as incremental area under the curve (I-AUC)) to an oral glucose tolerance test, and lipids (i.e., total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), TC: HDL-C, LDL-C: HDL-C) were measured before and after each of the two diet phases. Differences between the pulse-diet phase and the control phase were glucose I-AUC (−20.4 mmol/L), insulin I-AUC (1348 µIU/mL), TC (−0.1 mmol/L), LDL-C (−0.24 mmol/L), HDL-C (0.14 mmol/L), TG (0.07 mmol/L). No statistically significant differences were apparent for any of these changes between diet phases; however, the pulse-based diet significantly decreased TC/HDL-C (−0.22, P = 0.01), and LDL-C/HDL-C (−0.19, P = 0.03) compared to the regular diet. The lipoprotein ratios are better predictors of cardiovascular risk than the isolated parameters. Ready-to-eat packaged pulse-based meals can easily be incorporated as part of a healthy lifestyle to lower risk of cardiovascular diseases. Funded by Weston Foundation, Saskatchewan Pulse Growers, and Saskatchewan Agriculture Development Fund.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".