Impact of an Integrated Model of Care on Outcomes of Patients With Inflammatory Bowel Diseases: Evidence From a Population-Based Study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Studies evaluating the impact of integrated models of care [IMC] for inflammatory bowel disease [IBD] on disease-related outcomes are needed. We compared the risk of IBD-related outcomes and prescription medication claims between patients exposed and non-exposed to an IMC. METHODS: A retrospective population-based matched cohort study was conducted between 2009 and 2015, using administrative health data of Saskatchewan, Canada. Patients aged 18+ years with a diagnosis of IBD were identified with a validated administrative definition. Cases were classified as exposed and non-exposed to the IMC for IBD and matched based on propensity scores and disease duration. IBD-related hospitalisations, surgeries, prescription medication claims, and corticosteroid dependency [CsDep] were measured. Cox and logistic regression models evaluated differences between the groups, estimating hazard [HRs] and odds [ORs] ratios with corresponding confidence intervals [CIs]. RESULTS: In total, 2312 matched patients were included; 24.3% were exposed individuals. Compared with non-exposed, exposed patients had a lower risk of IBD-related surgeries [HR = 0.78, 95% CI 0.61-0.99], higher risk of prescriptions of immune modulators [HR = 1.68, 95% CI 1.42-1.99], and biologics [HR = 1.85, 95% CI 1.52-2.27], and a lower risk of 5-aminosalicylic acid prescriptions [HR = 0.81, 95% CI 0.69-0.95]. A lower risk of IBD-related hospitalisations among exposed ulcerative colitis [UC] patients [HR = 0.66, 95% CI 0.49-0.89] was identified in stratified analyses. The odds of CsDep among exposed UC patients was 0.39 [95% CI 0.15-0.98]. CONCLUSIONS: The observed differences in disease-related outcomes and use of steroid-sparing maintenance therapies between exposed and non-exposed individuals support the concept that enhanced quality of care can be achieved within IMC for IBD.
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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 | 0.023 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".