A Simple Admission Order-set Improves Adherence to Canadian Guidelines for Hospitalized Patients With Severe Ulcerative Colitis
Bibliographic record
Abstract
Abstract Background Individuals hospitalized with severe ulcerative colitis represent a complex group of patients. Variation exists in the quality of care of admitted patients with inflammatory bowel disease. We hypothesized that implementation of a standardized admission order set could result in improved adherence to current best practice guidelines (Toronto Consensus Statements) for the management of this patient population. Methods A retrospective cohort study of patients admitted with severe ulcerative colitis to a Montreal tertiary center was conducted. Two cohorts were defined based on pre- and post-implementation of a standardized order set. Adherence to 11 quality indicators was assessed before and after implementation of the intervention. These included: Clostridioides difficile and stool cultures testing, ordering an abdominal X-ray and CRP, organizing a flexible sigmoidoscopy, documenting latent tuberculosis, initiating thromboprophylaxis, use of intravenous steroids, prescribing infliximab if refractory to steroids, limiting narcotics, and surgical consultation if refractory to medical therapy. Results Adherence to 6 of the 11 quality indicators was improved in the post-intervention cohort. Significant increases were noted in adherence to C difficile testing (75.5% versus 91.9%, P < 0.05), CRP testing (71.4% versus 94.6%, P < 0.01), testing for latent tuberculosis (38.1% versus 84.6%, P < 0.01), thromboprophylaxis (28.6% versus 94.6%, P < 0.01), adequate corticosteroids prescription (72.9% versus 94.6%, P < 0.01), and limitation of narcotics prescribed (68.8% versus 38.9%, P < 0.01). Conclusions Implementation of a standardized order set, focused on pre-defined quality indicators for hospitalized patients with severe UC, was associated with meaningful improvements to most quality indicators defined by the Toronto Consensus Statements.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".