The evolving epidemiology of inflammatory bowel disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Epidemiologic studies in inflammatory bowel disease (IBD) include assessments of disease burden and evolving patterns of disease presentation. Although it is hoped that sound epidemiologic studies provide aetiological clues, traditional risk factor-based epidemiology has provided limited insights into either Crohn's disease or ulcerative colitis etiopathogenesis. In this update, we will summarize how the changing epidemiology of IBD associated with modernization can be reconciled with current concepts of disease mechanisms and will discuss studies of clinically significant comorbidity in IBD. RECENT FINDINGS: The increased frequency of IBD, which has been consistently observed as society becomes developed or modernized, may be linked with changes in the gastrointestinal microbiota which, in turn, may affect the development of the immune system and influence the risk of inflammatory diseases. Although extra-intestinal disease associations have long been recognized to be linked to IBD, there is a disturbing increase in comorbidity with Clostridium difficile-associated disease, arterial and venous thromboembolism and abnormalities of cervical cytology. These have important implications in an era of increased use of immunomodulatory drugs. SUMMARY: Advances in understanding the basic biology of IBD with rapidly emerging therapeutic strategies have prompted a shift in traditional epidemiologic approaches away from risk factor anthologies toward rapprochement with disease mechanisms and pursuit of changing patterns of comorbidity of clinical relevance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".