A Review of the Epidemiology of Inflammatory Bowel Disease with a Focus on Diet, Infections and Antibiotic Exposure
Bibliographic record
Abstract
Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), are chronic debilitating diseases that occur in populations around the world. The underlying etiology is thought to be multifactorial. There is a well-defined genetic contribution to the diseases, but this does not fully explain the epidemiology. Environmental factors, including the composition of the gut microbiota, are also important. There is wide geographic variability in the incidence and prevalence of IBD. High incidence rates have been observed in the United Kingdom, Northern Europe, Canada, and the United States. Globally, there is evidence of increasing incidence of CD and UC over time. The rising incidence of IBD in Western countries has generally predated that in developing nations, supporting the hypothesis that 'Westernization' of our lifestyle has led to the increased incidence of IBD. Smoking, antibiotic use, and diet are potentially reversible risk factors for IBD. Recommendations to avoid smoking are appropriate for all people, for numerous reasons. Antibiotic use should be limited to appropriate indications, a recommendation that too is appropriate for all populations. Detangling the relationship between diet, the gut microbiome and IBD raises the potential to reduce the incidence of IBD through dietary modification, an approach that might be considered among those at the highest risk.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".