Patterns and Predictors of Long-term Nonuse of Medical Therapy Among Persons with Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: To evaluate patterns and predictors of long-term nonuse of inflammatory bowel disease (IBD)-specific medications among patients with IBD. METHODS: All incident cases of IBD diagnosed between 1987 and 2012 were identified from the population-based University of Manitoba IBD Epidemiology Database. Point prevalence of long-term medication nonuse (defined as no receipt of IBD-specific medications for a year or longer) was determined over calendar time and the course of disease. Cox proportional hazard regression analysis was performed to identify factors associated with delayed initiation and with becoming a long-term nonuser. RESULTS: Among 6451 persons with IBD followed since 1987 (46.8% male, 47.8% with Crohn's disease), 11.7% were not dispensed an IBD-specific medication within the first year and 6.2% within 5 years after diagnosis. Factors associated with delayed initiation included having Crohn's disease (hazard ratio [HR] = 0.78, 95% confidence interval [CI], 0.73-0.83), lower socioeconomic status (HR = 0.91, 95% CI, 0.84-0.98), age more than 65 years (HR = 0.76, 95% CI, 0.67-0.86), and having any medical comorbidity. The prevalence of long-term nonuse consistently remained between 40% and 50% of persons with IBD across the study years. Patients with Crohn's disease (HR = 1.14, 95% CI, 1.04-1.25), lower socioeconomic status (HR = 1.14, 95% CI, 1.02-1.27), patients with IBD-associated surgery (HR = 1.72, 95% CI, 1.51-1.96), or delayed initiation of first IBD medication were more likely to become long-term nonusers after initiation. CONCLUSIONS: At any given time, roughly half of all patients with IBD have not used IBD-specific medications in the previous year. Further work is required to evaluate the clinical implications of long-term medication nonuse in 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".