A Low-Glycemic Index, High-Fiber, Pulse-Based Diet Improves Lipid Profile, but Does Not Affect Performance in Soccer Players
Bibliographic record
Abstract
Pulses (i.e., lentils, chickpeas, beans, peas) are low-glycemic index, high-fiber foods that are beneficial for improving blood lipids. Young soccer players typically have low dietary fiber intake, perhaps because of concerns regarding gastro-intestinal problems during exercise performance. Twenty-seven (17 females) soccer players were randomized to receive a pulse-based diet or their regular diet for four weeks in a cross-over study and evaluated for changes in blood lipids and athletic performance, with 19 (22 ± 6y; 12 females) completing the study (eight participants withdrew because of lack of time). Women increased high density lipoproteins (+0.5 ± 0.7 vs. −0.6 ± 0.3 mmol/L; p < 0.01) and reduced total cholesterol to high density lipoprotein ratio (−2.4 ± 2.9 vs. +2.6 ± 2.2; p < 0.01) on the pulse-based vs. regular diet, respectively, while there were no differences between diet phases in men. Athletic performance assessed by distance covered during games by a global positioning system was not significantly different during the pulse-based vs. regular diet (9180 ± 1618 vs. 8987 ± 1808 m per game; p = 0.35). It is concluded that a pulse-based diet can improve blood lipid profile without affecting athletic performance in soccer players.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".