Epidemiology of Inflammatory Bowel Disease and Overview of Pathogenesis
Bibliographic record
Abstract
colitis (UC) are believed to affect ap-proximately 1.4 million people in the United States (US).1 In many industrial-ized and developing parts of the world the incidence is rising. Both genetic and environmental factors are believed to con-tribute to this rise. However, people of all ages, ethnic, socioeconomic, and ra-cial backgrounds are affected. INCIDENCE Incidence is defined as the number of new cases of a disease in a defined population occurring within a specified period of time. Population-based studies conducted in the US and designed to evaluate the incidence rate of IBD have been limited. Between 1990 and 2000 the incidence rate of UC and CD in Olmsted County, MN was estimated to be 8.8 and 7.9 per 100,000 person-years, respectively.2 In 2003, the popu-lation of the US and Canada was ap-proximately 320 million people. Loftus et al. determined that between 7000 and 46,000 residents of the US and Canada are newly diagnosed with UC each year. In addition, 10,000 to 47,000 residents of the US and Canada are diagnosed with CD annually.1 (Table 1)2-18 PREVALENCE Prevalence is defined as the total num-ber of cases of a disease in the population at a given time. Similar to incidence, data are limited on the prevalence of IBD. Loftus et al. found 214 cases of UC and 174 cases of CD per 100,000 person-years in Olmsted County, MN, on Janu-
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".