A Gluten-Free Food Guide used in diet education to improve diet quality in children with newly diagnosed celiac disease: a pilot randomised control trial
Bibliographic record
Abstract
Abstract Children with coeliac disease (CD) on a gluten-free diet (GFD) often have poor dietary quality (DQ). A Gluten-Free Food Guide (GFFG) was developed to address this. This pilot randomised controlled trial evaluated the impact of GFFG dietary counselling on DQ and ultra-processed food (UPF) intake in newly diagnosed CD children. Child–parent pairs were randomised to the standard of care only (CON: n 20) or the intervention (INT: standard of care + GFFG; n 20). Primary outcomes included DQ (Healthy Eating Index-Canadian) and UPF intake (NOVA classification), assessed at baseline (BL), 3 and 6 months. In INT, dietary variety scores, a subcomponent of DQ, increased between BL and 3 months (BL: 6·7 (3·3–6·7) v . 3 months: 10 (10–10); P = 0·01) and in higher variety scores than CON at 3 months ( P < 0·01). Total DQ and UPF intake remained unchanged. Increased dietary variety in INT was associated with increases in dairy products (BL: 7·5 ( sd 3·6) % v . 3 months: 12·4 ( sd 6·7) %; P = 0·01) and unsweetened milk (BL: 2·5 ( sd 2·2) % v . 3 months: 4·7 ( sd 3·0) %; P = 0·01) servings, consumed as a percentage of the total food group servings. These improvements were not observed at 6 months. A greater number of INT children met the GFFG protein recommendation at 3 months (BL: 0/19 v . 3 months: 5/19; P = 0·01), with no change in CON. A single GFFG session improved short-term dietary variety and unsweetened milk intake. Ongoing work addressing the GF food environment, dietitian access and policies to improve DQ are needed.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".