Improving gluten free diet adherence by youth with celiac disease
Bibliographic record
Abstract
INTRODUCTION: Celiac disease (CD) is a gluten-triggered autoimmune disorder of the small intestine, which can occur in genetically susceptible individuals at any age. A strict life-long gluten free diet (GFD) is the only medically approved treatment, and non-adherence is associated with significant morbidity. However, gluten use is widespread, complicating efforts to follow the diet. Youth with CD are especially challenged with dietary adherence, as they strive for peer acceptance and personal autonomy in the context of managing a chronic disease. METHODS: A scoping review was conducted to identify mechanisms to assist youth with remaining gluten free. RESULTS: There is a paucity of literature regarding best approaches to improve diet adherence by youth, however, lessons can also be learned by borrowing ideas from self-management approaches of other chronic diseases. Several mechanisms for improving GFD adherence among youth are identified, including regular engagement of the youth with CD and their family with an experienced multidisciplinary team, electronic tool utilization and awareness of accurate resources for self-guided education and resources. CONCLUSIONS: Improvement in GFD adherence by youth is achievable and may influence long-term health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".