Evaluating the process of care for persons admitted to Toronto area hospitals with acute severe ulcerative colitis
Bibliographic record
Abstract
Background: Acute severe ulcerative colitis (ASUC) is associated with significant morbidity. In patients with ulcerative colitis (UC), the estimated lifetime risk of developing severe colitis is 25%. Several gastrointestinal societies have provided recommendations on pathways of care for managing ASUC. The degree to which they are adhered to in different care settings remains unclear. Methods: We conducted a retrospective review using data from 7 acute-care hospitals collected through the general medicine inpatient initiative (GEMINI), a hospital research collaborative that collects administrative and clinical data from hospital information systems. We identified all patients with the most responsible inpatient discharge diagnosis of ulcerative colitis between April 2015 and December 2019. The primary outcome was the difference in hospital length of stay of patients admitted with ASUC based on hospital-type; community, academic, or inflammatory bowel disease (IBD)-focussed sites. Results: = .094). Adverse events were uncommon overall. In our multiple logistic regression analysis, we identified that admission to an IBD-focussed centre compared to an academic centre, carried an odds ratio of 2.07 (95% CI, 1.16-3.78) for the outcome of inpatient-colectomy. Conclusions: The processes of care for patients with ASUC varied on the basis of the type of hospital they were admitted to, with the IBD specialty centre providing the most guideline adherent care. Low-cost interventions should be utilized to promote adherence to clinical practice recommendations.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".