Design of a dietary intervention to assess the impact of a gluten-free diet in a population with type 1 Diabetes and Celiac Disease
Bibliographic record
Abstract
BACKGROUND: Celiac Disease occurs at a 5-10 fold greater prevalence in patients with type-1 diabetes (T1D), despite this increased risk, there is limited objective evidence regarding the impact of a Gluten-Free Diet (GFD) in the large proportion of asymptomatic (30-70%) patients with both autoimmune diseases. Given the requirements and intricacies inherent to each condition, we describe the rationale and design a dietary curriculum specifically addressing the educational requirements for children and adults with CD and diabetes as part of the CD-DIET Study. METHODS AND DESIGN: The CD-DIET Study (Celiac Disease and Diabetes - Dietary Intervention and Evaluation Trial) is a multicenter randomized controlled trial aimed at evaluating the safety and efficacy of a GFD in patients with asymptomatic celiac disease and T1D on key diabetes and patient-centered outcomes. DISCUSSION: Key dietary components of the trial include a description and evaluation of food consumption patterns including glycemic index and glycemic load, novel assessments of gluten quantification, and objective and subjective measures of GFD adherence. This dietary curriculum will establish rigorous guidelines to assess adherence and facilitate evaluation of a GFD on metabolic control, bone health and patient quality of life in patients with CD and diabetes. TRIAL REGISTRATION NUMBER: NCT01566110. Date of Registration: March, 2012.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".