Rates and Reasons for Nonuse of Prescription Medication for Inflammatory Bowel Disease in a Referral Clinic
Bibliographic record
Abstract
BACKGROUND: We aimed to determine the rates and reasons for nonuse of inflammatory bowel disease (IBD)-specific medication in a referral clinic. METHODS: Consecutive persons with Crohn's disease (CD) (n = 423) and ulcerative colitis (UC) (n = 342) were followed in a single clinic over 2 years. At each patient visit, it was determined whether and what type of IBD-specific medications were used at that visit. If medications were not used, the reason for nonuse was recorded. Disease remission, further stratified by "clinical remission" and "deep remission" (clinical remission plus imaging evidence of remission), was considered a reason for nonuse if the attending physician believed the person was in remission and agreed for them to be off medications. RESULTS: Nonuse of IBD-specific medication was seen in 121 persons with CD (29%) and 65 persons with UC (18%). In CD, increased age and disease duration were associated with nonuse; disease phenotype did not predict nonuse. In UC, disease duration was associated with nonuse but age was not. In CD, the most common reason for medication nonuse was deep remission (22.5%), followed by clinical remission (21.4%), not having seen a gastroenterologist for a lengthy period (21.4%) and nonadherence (16%). In UC, nonuse was attributed to deep remission (27.7%), followed by nonadherence (26.3%) and clinical remission (23%). CONCLUSIONS: Approximately a quarter of persons with IBD attending at a tertiary care practice do not use IBD-specific medications with a higher rate in CD than UC. The decision not to use medications was deemed appropriate in approximately one-half of all nonusers.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".