Determination of serologic and genetic markers aid in the determination of the clinical course and severity of patients with IBD
Bibliographic record
Abstract
In the last decade unprecedented progress has been made in advancing our understanding of the pathophysiology underlying inflammatory bowel disease (IBD). We now know that, at least in part, genetically determined defects in innate, and perhaps adaptive, immunity alter the way our mucosal immune system interacts with our resident bacterial flora.1 This dysregulated interaction leads to the (mal)adaptive immune response that is, in large part, responsible for the chronic inflammatory lesions, characteristic of patients with IBD. The seroreactivity to specific microbial antigens that can be seen in a subgroup of patients with IBD likely represents a surrogate measure of this adaptive immune response. Since reactivity to yeast oligomannan (anti-Saccharomyces cerevisiae antibody; ASCA) was first described in Crohn's disease (CD), the number of CD-specific antigens for which reactivity can be measured has expanded to include the Pseudomonas fluorescens-related protein (I2), Escherichia coli outer membrane-porin (OmpC), and CBir1 flagellin (CBir1).2,–5 In addition, since first described perinuclear antineutrophil cytoplasmic antibodies (pANCA) have emerged as an important marker of the immune response in patients with ulcerative colitis (UC) and patients with a colonic, UC-like CD phenotype.6,–8
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".