Highlights in IBD Epidemiology and Its Natural History in the Paediatric Age
Bibliographic record
Abstract
Background. The number of patients of all age brackets diagnosed with Inflammatory Bowel Disease (IBD) has risen dramatically worldwide over the past 50 years. IBD's changing epidemiology suggests that environmental factors play a major role in modifying disease expression. Aim. To review studies carried out worldwide analyzing IBD epidemiology. Methods. A Medline search indicating as keywords "Inflammatory Bowel Disease," "epidemiology," "natural history," "Crohn's Disease," "Ulcerative Colitis," and "IBD Unclassified" was performed. A selection of clinical cohort and systematic review studies that were carried out between 2002 and 2013 was reviewed. Studies referring to an earlier date were also considered whenever the data were relevant to our review. Results. The current mean prevalence of IBD in the total population of Western countries is estimated at 1/1,000. The highest prevalence and incidence rates of IBD worldwide are reported from Canada. Just as urbanization and socioeconomic development, the incidence of IBD is rising in China. Conclusions. Multicenter national registers and international networks can provide information on IBD epidemiology and lead to hypotheses about its causes and possible management strategies. The rising trend in the disease's incidence in developing nations suggests that its epidemiological evolution is linked to industrialization and modern Westernized lifestyles.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".