Review article: treatment algorithms to maximize remission and minimize corticosteroid dependence in patients with inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND: Crohn's disease (CD) and ulcerative colitis (UC) are chronic inflammatory diseases of the intestine, which frequently require surgery for complications or failure of medical therapy. AIM: To seek evidence and provide direction for clinicians on optimal strategies to enable steroid free remission in inflammatory bowel disease. METHODS: Scientific literature was reviewed using MEDLINIE with a specific focus on medical therapies for inducing and maintaining remission of CD and UC. The results were discussed at a roundtable meeting to reach a consensus on key issues. RESULTS: Several therapies have demonstrated efficacy for the treatment of active, moderate-to-severe CD and UC. These include agents, which induce remission [corticosteroids, infliximab and adalimumab (CD only)] or maintain remission and spare corticosteroids [azathioprine, mercaptopurine, methotrexate (CD only), infliximab and adalimumab (CD only)]. Wide variability exists in the use of these agents. CONCLUSION: Treatment strategy algorithms are developed for use of these therapies that maximize remission and minimize corticosteroid dependence in patients with moderate-to-severe CD and UC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".