Probiotic Use in Celiac Disease: Results from a National Survey
Bibliographic record
Abstract
BACKGROUND AND AIMS: Patients with celiac disease (CD) commonly use supplements for perceived health benefits despite scant evidence. We aimed to characterize the prevalence and predictors of probiotic use among CD patients. METHODS: We analyzed data from iCureCeliac®; a patient-powered research network questionnaire distributed by the Celiac Disease Foundation. We included adults with self-reported CD who answered questions regarding demographics, diagnosis, symptoms, and treatment. We compared probiotic users versus probiotic non-users and subsequently performed multivariable logistic regression, assessing for independent predictors of probiotic use. RESULTS: 4,909 patients met the criteria for inclusion in the study. Of these, 1,160 (23.6%) responded to a question regarding probiotic use. The mean age of participants was 38.8 years and 82% were female. 381 patients (33%) reported using probiotics. More probiotic users sought nutritional counseling at time of diagnosis (36% vs. 30%, p=0.05) and remained symptomatic despite a gluten-free diet (40% vs. 25%, p <0.001). Probiotic users had lower scores on the pain subscale of the SF36 (63.7±21.6 vs. 69.5±22.1, p=0.006). On multivariable analysis, patients diagnosed after age 50 (OR=2.04, 95%CI: 1.37-3.04), and those with persistent symptoms despite a gluten-free diet (OR=1.94, 95%CI: 1.44-2.63) were more likely to use probiotics. CONCLUSION: In this large study of a national CD registry, roughly one-third of CD patients reported using probiotics. Patients diagnosed later in life were more likely to use probiotics and those who remained symptomatic despite a gluten-free diet were twice as likely to take probiotics. Patients may be seeking additional means of treatment for persistent symptoms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".