Classification of inflammatory bowel disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Inflammatory bowel diseases (IBDs) are disorders of multifactorial cause that present as a multitude of phenotypes, clinical behaviours and severity. Crohn's disease and ulcerative colitis are considered as the two extremes of what is believed to be a spectrum of chronic gut inflammation and this separation is still the first classification used when confronted with an IBD patient. An accurate classification within IBD has several benefits, with respect to patient counselling, assessing risk for disease progression, and particularly with respect to choosing the most appropriate therapy for an individual patient. Basic scientists on the other hand prefer classifications that would allow to better understand the pathophysiology of the different manifestations of Crohn's disease and ulcerative colitis. RECENT FINDINGS: Attempts to reclassify IBD based on recent genetic, serologic or immunologic findings have been made. Most, however, have not been translated to daily practice and need confirmation first. SUMMARY: Clinicians should apply a systematic approach to their patients by using existing phenotypic classifications such as Montreal or Paris. They should further recognize clinical and endoscopic features of bad outcome such as perianal disease, deep ulcers on colonoscopy and extensive small bowel involvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".