Challenges in IBD Research 2024: Environmental Triggers
Bibliographic record
Abstract
Environmental factors play an important role in inflammatory bowel diseases (IBD; Crohn's disease, [CD], ulcerative colitis [UC]). As part of the Crohn's & Colitis Challenges 2024 agenda, the Environmental Triggers workgroup summarized the progress made in the field of environmental impact on IBD since the last Challenges cycle in this document. The workgroup identified 4 unmet gaps in this content area pertaining to 4 broad categories: (1) Epidemiology; (2) Exposomics and environmental measurement; (3) Biologic mechanisms; and (4) Interventions and Implementation. Within epidemiology, the biggest unmet gaps were in the study of environmental factors in understudied populations including racial and ethnic minority groups and in populations witnessing rapid rise in disease incidence globally. The workgroup also identified a lack of robust knowledge of how environmental factors may impact difference stages of the disease and for different disease-related end points. Leveraging existing cohorts and targeted new prospective studies were felt to be an important need for the field. The workgroup identified the limitations of traditional questionnaire-based assessment of environmental exposure and placed high priority on the identification of measurable biomarkers that can quantify cross-sectional and longitudinal environmental exposure. This would, in turn, allow for identifying the biologic mechanisms of influence of environmental factors on IBD and understand the heterogeneity in effect of such influences. Finally, the working group emphasized the importance of generating high-quality data on effective environmental modification on an individual and societal level, and the importance of scalable and sustainable methods to deliver such changes.
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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 | 0.148 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".