A153 QUALITY OF CARE AND OUTCOMES IN A TERTIARY HOSPITAL INFLAMMATORY BOWEL (IBD) CENTER: MONITORING AND TREATMENT ALGORITHMS DURING FOLLOW-UP
Bibliographic record
Abstract
Optimal management of IBD requires harmonized monitoring processes and treatment pathways. We aimed to retrospectively analyze quality of care indicators (QIs) during follow-up including patient monitoring, treatment decisions and outcomes in the McGill University Health Center (MUHC) IBD Center. We retrospectively analyzed out- and inpatient records of all consecutive patient at the MUHC IBD center between June and December 2016. Demographic variables, outpatient visits, inpatient stays, laboratory, imaging and endoscopy data, current medications and/or changes in medications, and vaccination profile were captured. 653 patients (46.2% men, 66.6% Crohn’s disease (CD), age at index follow-up visit: 44.7 years, duration of follow-up: 4.2 years) were included. Complicated behavior and perianal disease were found at index visit in respectively 47% and 24% of CD patients, while extensive UC in 41%. 44% of patients received biologics among which 11% non-anti TNF-biologics. Patient re-evaluation was common: ileocolonoscopy was performed in 34 %, MRI in 11 % and CT in 23 % within 6 months after index visit. Biomarkers were measured frequently (CRP: 67%, FCAL: 33%). New or repeated HBV/HCV testing was performed in 20%, C.difficile in 28%, stool culture in 24%, TB in 10%, therapeutic drug monitoring was performed in 26% of patients on biologics. Treatment was changed in 18%. Need for surgery (4%) and hospitalization (8%) were relatively low within 6 months after index visit. Waiting time for ileocolonoscopy (35 vs 60 days, p<0.001), but not for cross sectional imaging (45–48 days for MRI, 25–30 days for CT), was shorter in active disease. Our data support that tight monitoring strategy is applied in the MUHC IBD center during follow-up with objective patient reassessment and accelerated medical strategy in patients with and without flares. McGill University Health Center (MUHC) Department of Medicine CAS Research Award
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".