Exploring gluten-related disorders
Bibliographic record
Abstract
The gluten-free diet is the only treatment available for gluten-related disorders, such as celiac disease, an autoimmune reaction to gluten, or non-celiac gluten or wheat sensitivity, a symptomatic reaction to wheat or gluten. However, gluten may not be the only culprit, and patients on a gluten-free diet have been suggested to symptomatically improve through the placebo effect, alterations in immune activity, and alterations in gut microbiota composition. It is unclear which of these mechanisms underlie symptoms in gluten-related disorders and well-designed clinical studies are needed to better understand them. This thesis aims to understand the mechanisms and symptomatic responses by which wheat and gluten affect individuals with gluten-related disorders. I hypothesize that patients with gluten-related disorders have increased psychological symptoms and immune reactivity which may be modulated by the gut microbiota. To test this, I conducted a clinical crossover trial to investigate whether whole wheat or gluten triggered symptoms versus gluten-free control, or nocebo, in irritable bowel syndrome patients adopting a gluten-free diet. Participants reacted similarly to each intervention, suggesting a strong 'nocebo effect' to be the main trigger of their symptoms. However, several participants did not comply to the protocol, muddying the results. Subsequent follow-up visits after disclosing personalized study results found no changes in participant beliefs, behaviours, and symptoms, and most remained on a gluten-free diet. Next, a systematic review of 65 observational studies found an elevated risk of IBD in celiac disease and vice versa. Finally, a systematic review of 6 RCTs found limited evidence that probiotics are safe and possibly therapeutic for ameliorating symptoms in celiac disease. Overall, the work presented in this thesis critically assesses the mechanisms by which gluten and wheat trigger symptoms in gluten-related disorders and highlights the importance of rigorous clinical trial design to control for psychological factors and patient compliance.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".