Treatment of ulcerative colitis from the patient’s perspective: a survey of preferences and satisfaction with therapy
Bibliographic record
Abstract
BACKGROUND: Data available regarding patient perspectives on ulcerative colitis (UC) and their preferences and satisfaction with therapy are limited. AIMS: To examine the preferences of UC patients to understand better what they look for in a therapy when managing their disease, as this may influence overall medication adherence. METHODS: The study surveyed 100 Canadian UC patients on topics including educational resources used to learn about the disease, medication attributes that are most valued and preferred by the patient and satisfaction with current therapy. RESULTS: Overall, efficacy- and safety-related medication attributes were rated by patients to be more important than those related to dosing regimen (e.g. dosing frequency, number of pills), cost and formulary coverage. In pair-wise comparisons of specific medication attributes, UC patients rated speed of symptom relief and few side effects as the most important factors when considering a UC medication (preferred on average 84% and 74% of the time respectively). CONCLUSION: This study provides insight into UC patient preferences and satisfaction with therapy that may be important when counselling on treatment options, and generates relevant discussions on adherence. Larger studies may be warranted to examine further how these findings can be extrapolated to broader UC populations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".