Co-administration of Complementary Therapies for Cardiovascular Disease Risk Reduction in Type 2 Diabetes
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the main cause of mortality in patients with type 2 diabetes (T2D). Selected dietary and herbal supplements with complementary mechanisms of action have been indicated in management of diabetes along with standard therapy. The objective of this thesis was to determine if a 24-weeks co-administration of 4 diet and herbal supplements will improve CVD risk factors beyond conventional therapy in T2D. The project consisted of a randomized, double-blind, controlled trial with two parallel groups involving 104 individuals with T2D (HbA1c: 70.05%) which were randomly assigned to test (10g viscous fiber, 60g Salba-Chia, 1.5g American and 0.75g Korean red ginseng extracts daily), or energy and fibre matched control (53g oat bran, 25g inulin, 25g maltodextrose and 2.25g wheat bran daily) for 24 weeks. Fasting blood was drawn at weeks 0, 12 and 24. Primary and secondary endpoints was change in HbA1c, blood pressure and serum lipids over 24 weeks. The study was conducted at two centres: St. Michael’s Hospital in Toronto, Canada and Vuk Vrhovac University Clinic in Zagreb, Croatia. Results were computed using an intent-to-treat analysis with multiple imputations. Eighty-seven participants completed the trial (test n=44; control n=43). The test intervention significantly reduced HbA1c (0.270.13% (p=0.03) and 24-hour systolic blood pressure by 3.81.2 mmgHg (p
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.004 | 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".