Development of the global inflammatory bowel disease visualization of epidemiology studies in the 21st century (GIVES-21)
Bibliographic record
Abstract
BACKGROUND: There is a rapid increase in the incidence of inflammatory bowel diseases (IBD) in newly industrialized countries, yet epidemiological data is incomplete. We herein report the methodology adopted to study the incidence of IBD in newly industrialized countries and to evaluate the effect of environmental factors including diet on IBD development. METHODS: Global IBD Visualization of Epidemiology Studies in the 21st Century (GIVES-21) is a population-based cohort of newly diagnosed persons with Crohn's disease and ulcerative colitis in Asia, Africa, and Latin America to be followed prospectively for 12 months. New cases were ascertained from multiple sources and were entered into a secured online system. Cases were confirmed using standard diagnostic criteria. In addition, endoscopy, pathology and pharmacy records from each local site were searched to ensure completeness of case capture. Validated environmental and dietary questionnaires were used to determine exposure in incident cases prior to diagnosis. RESULTS: Through November 2022, 106 hospitals from 24 regions (16 Asia; 6 Latin America; 2 Africa) have joined the GIVES-21 Consortium. To date, over 290 incident cases have been reported. All patients have demographic data, clinical disease characteristics, and disease course data including healthcare utilization, medication history and environmental and dietary exposures data collected. We have established a comprehensive platform and infrastructure required to examine disease incidence, risk factors and disease course of IBD in the real-world setting. CONCLUSIONS: The GIVES-21 consortium offers a unique opportunity to investigate the epidemiology of IBD and explores new clinical research questions on the association between environmental and dietary factors and IBD development in newly industrialized countries.
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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 | 0.210 | 0.260 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.002 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".