The Challenge of Compliance and Persistence: Focus on Ulcerative Colitis
Bibliographic record
Abstract
BACKGROUND: Non-adherence to therapy is a widespread problem, with typical adherence rates for prescribed medications being approximately 50%. An estimated 20% to 50% of patients with ulcerative colitis (UC) do not take their medications as prescribed, resulting in higher disease-recurrence rates and potentially higher health care costs. OBJECTIVE: To characterize the problem of non-adherence in UC, to review the many factors affecting compliance and persistence in this population, and to discuss practical strategies to improve adherence in these patients. SUMMARY: Adherence to and persistence with medication are complex and multifactorial behaviors. Factors shown to affect adherence in UC patients include disease extent and duration, cost of medications, fear of adverse effects, individual psychosocial variables, and the patient-physician relationship. In contrast, recent data do not support an important role for treatment-related factors such as daily dose, regimen, and formulation in influencing adherence in this population, particularly with longer duration of use. Strategies to improve adherence should involve the patient, the provider, and the health care delivery system. For UC patients, knowledge and discussion of the rationale for supporting persistence, such as recent data regarding agents that have a potential chemoprotective benefit, may encourage persistence, even during periods of quiescence. The patient-physician relationship is critical in encouraging adherence, particularly with respect to education, open communication, and agreement regarding the value of the assigned treatment. Health care delivery systems can improve adherence by encouraging the participation of multidisciplinary teams, providing reporting and tracking systems, and eliminating financial barriers where possible.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".