Low-glycaemic index diet to improve glycaemic control and cardiovascular disease in type 2 diabetes: design and methods for a randomised, controlled, clinical trial
Bibliographic record
Abstract
Introduction Type 2 diabetes (T2DM) produces macrovascular and microvascular damage, significantly increasing the risk of cardiovascular disease (CVD), renal failure and blindness. As rates of T2DM rise, the need for effective dietary and other lifestyle changes to improve diabetes management become more urgent. Low-glycaemic index (GI) diets may improve glycaemic control in diabetes in the short term; however, there is a lack of evidence on the long-term adherence to low-GI diets, as well as on the association with surrogate markers of CVD beyond traditional risk factors. Recently, advances have been made in measures of subclinical arterial disease through the use of MRI, which, along with standard measures from carotid ultrasound (CUS) scanning, have been associated with CVD events. We therefore designed a randomised, controlled, clinical trial to assess whether low-GI dietary advice can significantly improve surrogate markers of CVD and long-term glycaemic control in T2DM. Methods and analysis 169 otherwise healthy individuals with T2DM were recruited to receive intensive counselling on a low-GI or high-cereal fibre diet for 3 years. To assess macrovascular disease, MRI and CUS are used, and to assess microvascular disease, retinal photography and 24-hour urinary collections are taken at baseline and years 1 and 3. Risk factors for CVD are assessed every 3 months. Ethics and dissemination The study protocol and consent form have been approved by the research ethics board of St. Michael's Hospital. If the study shows a benefit, these data will support the use of low-GI and/or high-fibre foods in the management of T2DM and its complications. Trial Registration number NCT01063374; Pre-results.
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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 | 0.044 | 0.037 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.009 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".