Challenges in IBD Research: Environmental Triggers
Bibliographic record
Abstract
Environmental triggers is part of five focus areas of the Challenges in IBD research document, which also includes preclinical human IBD mechanisms, novel technologies, precision medicine and pragmatic clinical research. The Challenges in IBD research document provides a comprehensive overview of current gaps in inflammatory bowel diseases (IBD) research and delivers actionable approaches to address them. It is the result of a multidisciplinary input from scientists, clinicians, patients, and funders, and represents a valuable resource for patient centric research prioritization. In particular, the environmental triggers section is focused on the main research gaps in elucidating causality of environmental factors in IBD. Research gaps were identified in: 1) epidemiology of exposures; 2) identification of signatures of biological response to exposures; and 3) mechanisms of how environmental exposures drive IBD. To address these gaps, the implementation of longitudinal prospective studies to determine disease evolution and identify sub-clinical changes in response to exposures is proposed. This can help define critical windows of vulnerability and risk prediction. In addition, systems biology analysis and in silico modeling were proposed as approaches to integrate the IBD exposome for the identification of biological signatures of response to exposures, and to develop prediction models of the effects of environmental factors in driving disease activity and response to therapy. This research could lead to identification of biomarkers of exposures and new modalities for therapeutic intervention. Finally, hypothesis-driven mechanistic studies to understand gene-environment interactions and to validate causality of priority factors should be performed to determine how environment influences clinical outcomes.
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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 | 0.107 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".