Predicting Outcome after Acute Severe Ulcerative Colitis: A Contemporary Review and Areas for Future Research
Bibliographic record
Abstract
Acute Severe Ulcerative Colitis (ASUC) is a severe form of ulcerative colitis relapse which requires hospitalization and intensive medical intervention to avoid colectomy. The timely recognition of patients at risk of corticosteroid failure and the early initiation of medical rescue therapy are paramount in the management of ASUC. The choice of medical rescue therapy is influenced by multiple factors, especially patient's prior treatment history. This decision should involve the patient and ideally a multidisciplinary team of healthcare professionals, including gastroenterologists, radiologists, surgeons and enterostomal therapists. Although several predictive models have been developed to predict corticosteroid failure in ASUC, there is no single validated tool that is universally utilized. At present, infliximab and cyclosporine are the only agents systematically evaluated and recommended for medical rescue therapy, with recent reports of off-label utilization of tofacitinib and upadacitinib in small case series. The available evidence regarding the efficacy and safety of these oral small molecules for ASUC is insufficient to provide definitive recommendations. Early decision-making to assess the response to medical rescue therapy is essential, and the decision to pursue surgery in the case of treatment failure should not be delayed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".