Teaching Subjects With Type 2 Diabetes How to Incorporate Sugar Choices Into Their Daily Meal Plan Promotes Dietary Compliance and Does Not Deteriorate Metabolic Profile
Bibliographic record
Abstract
OBJECTIVE: To determine whether teaching free-living subjects with type 2 diabetes how to incorporate added sugars or sweets into their daily meal plan results in a greater consumption of calories (fat or sugar) and deteriorates their glycemic or lipid profiles but improves their perceived quality of life. RESEARCH DESIGN AND METHODS: In an 8-month randomized controlled trial, 48 free-living subjects with type 2 diabetes were taught either a conventional (C) meal plan (no concentrated sweets) or one permitting as much as 10% of total energy as added sugars or sweets (S). Mean individual nutrient intake was determined using the average of six 24-h telephone recalls per 4 months. Metabolic control and quality of life were evaluated every 2 months. Quality of life was assessed using the Medical Outcome Survey and the Diabetes Quality of Life questionnaire. RESULTS: The S group did not consume more calories (fat or sugar) and in fact ate significantly less carbohydrate (-15 vs. 10 g) and less starch (-7 vs. 8 g) and had a tendency to eat fewer calories (-77 vs. 81 kcal) than the C group. Weight remained stable, and there was no evidence that consuming more sugar worsened metabolic profile or improved their perceived quality of life. CONCLUSIONS: Giving individuals with type 2 diabetes the freedom to include sugar in their daily meal plan had no negative impact on dietary habits or metabolic control. Health professionals can be reassured and encouraged to teach the new "sugar guidelines," because doing so may result in a more conscientious carbohydrate consumption.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".