Comparison between a low glycemic load diet and a Canada Food Guide diet in cardiac rehabilitation patients in Ontario.
Bibliographic record
Abstract
BACKGROUND: A high dietary glycemic load is associated with an increased risk of noninsulin-dependent diabetes mellitus and coronary artery disease. OBJECTIVE: To evaluate the effect of a low glycemic load diet on cardiac rehabilitation patients. METHODS: One hundred twenty patients who were advised to follow a low glycemic load diet were evaluated and compared with 1434 patients who were advised to follow the principles of Canada's Food Guide to Healthy Eating for People Four Years and Over as part of the Ontario Cardiac Rehabilitation Pilot Project. RESULTS: Patients on the low glycemic load diet lost more weight at six months (2.8 kg loss versus 0.2 kg gain, P < 0.0001), had a greater reduction in abdominal obesity (2.9 cm versus 0.4 cm, P < 0.0001), and had a greater improvement in high density lipoprotein cholesterol (0.14 mmol/L versus 0.02 mmol/L, P < 0.0001), triglycerides (-0.44 mmol/L versus -0.08 mmol/L, P < 0.0001) and glycemic control (fasting glucose -0.94 mmol/L versus 0.91 mmol/L, P = 0.0019). After one year of follow-up, the low glycemic load patients had maintained (weight gain 0.7 kg, triglycerides -0.07 mmol/L, fasting glucose -0.10 mmol/L and glycosylated hemoglobin A1c -0.18%; all not significant) or augmented (waist circumference -1.3 cm, P = 0.038; high density lipoprotein cholesterol 0.08 mmol/L, P < 0.0001) the initial results. CONCLUSIONS: Implementation of a low glycemic load diet was associated with substantial and sustained improvements in abdominal obesity, cholesterol and glycemic control.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".