P479 Quality of care and outcomes in a tertiary hospital inflammatory bowel disease (IBD) centre: Monitoring and treatment algorithms during follow-up
Bibliographic record
Abstract
Optimal management of IBD requires harmonised monitoring processes and treatment pathways. We aimed to retrospectively analyse 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 analysed out- and inpatient records of consecutive patients at the MUHC IBD centre between June and December 2016. Demographic variables, outpatient visits, inpatient stays including for IBD-related surgery, laboratory, imaging and endoscopy data, current medications and/or changes in medications, and vaccination profile were captured. A total of 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 behaviour 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 hospitalisation (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 centre during follow-up with objective patient reassessment and accelerated medical strategy in patients with and without flares.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".