Bibliographic record
Abstract
Classifying IBD patients is important for decisions on the intensity of follow-up, therapy and mode of delivery. The most recent classification of Crohn's disease (CD) and ulcerative colitis (UC), the Montreal classification, is based on clinical grounds: for CD, the age at diagnosis and disease location and behavior and for UC, the age at diagnosis and the extent of disease. During the Working Party in 2005 in Montreal, it was judged by the panel of experts that a molecular reclassification using serology and/or genetic markers was too premature and not yet justified. In the meanwhile, the number of genes associated with IBD has increased to >100 and more antimicrobial peptides have also been identified. We recently showed that genetic variants enable the classification of CD patients in distinct clusters, which is different from clusters seen in healthy individuals. Furthermore, there was a poor relationship between the genetic-based subgroups and the clinical subphenotypes being used. The promising role for molecular markers lies most likely in disease stratification, the prediction of prognosis at the time of diagnosis and the prediction of therapy outcome. The behavior of CD and UC varies between patients, and the characteristic transmural inflammation in CD--when untreated--will often progress and lead to stricture or fistula formation over time. Predicting which patients are at risk for progression to complications and how fast this occurs could have therapeutic implications, as more aggressive treatment strategies could become defendable in the appropriate patient. Genetic factors are definitively more appealing for risk stratification on more solid grounds. Molecular markers may also better explain changes in disease behavior from a pathogenic standpoint, and by combining several markers this strategy can approach clinical utility. Before starting to apply molecular markers to predict disease behaviors, tools should be made available to score disease progression. Only then could the value of molecular markers to predict the speed of progression be fully explored in prospective studies.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".