A136 PREDICTING THE NEED FOR MEDICAL RESCUE IN PATIENT ADMITTED WITH ACUTE SEVERE ULCERATIVE COLITIS
Bibliographic record
Abstract
High dose corticosteroids(CS) are the mainstay of treatment for hospitalized patients with acute severe ulcerative Colitis (ASUC), and up to 40% of these cases require further salvage biological therapy or colectomy. To identify clinical features at the time of hospital admission which predict the need for inpatient medical rescue therapy and to measure quality of in-patient care. Retrospective chart review of consecutive adult patients with UC admitted to London Health Sciences Center between January 1, 2010 and June 30, 2016. ASUC was defined as exacerbation of symptoms with the need for hospitalization and intravenous CS. Baseline predictors of rescue therapy assessed were the Seo Index, Truelove and Witts Severity Index, White Blood Count, CRP, Pulse, Temperature, and number of bloody bowel movements. Care quality metrics were use of thromboprophlyaxis, stool culture, AXR, sigmoidoscopy within 48 hours and surgical discussion. Eighty-eight patients met inclusion criteria (57% male; mean age 38,5 years). The majority had pancolitis (63%) and were receiving oral steroids (56%) and 5-ASAs (67%) on admission. With just 22% receiving immunosuppressives and 13% anti-TNFs. The mean CRP (74.5), pulse (94.2) and number of bloody bowel movements (11.2) was consistent with ASUC. 51% received salvage rescue therapy with infliximab. The only factor associated with rescue therapy was the baseline Mayo endoscopic score. 68% received thromboprophylaxis, 70% sigmoidoscopy within 48 hours and 82% stool/C.diff culture. The Mayo Endoscopic score was strongly associated with the need for inpatient infliximab salvage therapy. There is room for improvement in the quality of care for inpatients with ASUC. The results of the univariable associations between the independent variables in both subsets of patients. None
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".