The impact of a gluten‐free diet on adults with coeliac disease: results of a national survey
Bibliographic record
Abstract
UNLABELLED: OBJECTIVE We sought to evaluate the impact of the gluten-free diet on the 5,240 members of the Canadian Celiac Association (CCA). Data are presented on 2,681 adults (>or=16 years) with biopsy-proven celiac disease (CD). METHODS: A mail-out survey was used. Quality of life was evaluated using the 'SF12', and celiac-specific questions. RESULTS: Mean age was 56 years, mean age at diagnosis was 45 years, and 75% were female. The 'SF12' summary scores were similar to normative Canadian data, but were significantly lower for females and newly diagnosed patients. Respondents reported: following a gluten-free (GF) diet (90%), improvement on the diet (83%), and difficulties following the diet (44%), which included: determining if foods were GF (85%), finding GF foods in stores (83%), avoiding restaurants (79%), and avoiding travel (38%). Most common reactions to consumed gluten (among 73%) included pain, diarrhea, bloating, fatigue, nausea, and headache. Excellent information on CD and its treatment was provided by the CCA (64%), gastroenterologists (28%), dietitians (26%) and family doctor (12%). CONCLUSIONS: Quality of life in those with CD could be increased with early diagnosis, increased availability of gluten-free foods, improved food labelling, and better dietary instruction. Education of physicians and dietitians about CD and its treatment is essential.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".