Characterisation of complementary and alternative medicine use and its impact on medication adherence in inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND: Complementary and alternative medicine (CAM) use among inflammatory bowel disease (IBD) patients is common. We characterised CAM utilisation and assessed its impact on medical adherence in the IBD population. AIM: To characterise CAM utilisation and assess its impact on medical adherence in the IBD population. METHODS: Inflammatory bowel disease patients recruited from an out-patient clinic at a tertiary centre were asked to complete a questionnaire on CAM utilisation, conventional IBD therapy, demographics and communication with their gastroenterologist. Adherence was measured using the self-reported Morisky scale. Demographics, clinical characteristics and self-reported adherence among CAM and non-CAM users were compared. RESULTS: We recruited prospectively 380 IBD subjects (57% Crohn's disease; 35% ulcerative colitis, and 8% indeterminate colitis). The prevalence of CAM use was 56% and did not significantly vary by type of IBD. The most common reason cited for using CAM was ineffectiveness of conventional IBD therapy (40%). The most popular form of CAM was probiotics (53%). CAM users were younger than non-CAM users at diagnosis (21.2 vs. 26.2, P < 0.0001) and more likely than non-CAM users to have a University-level education or higher (75% vs. 62% P = 0.006). There was no overall difference in adherence between CAM and non-CAM users (Morisky score: 1.0 vs. 0.9, P = 0.26). CONCLUSIONS: The use of complementary and alternative medicine is widely prevalent among IBD patients, and is more frequent among those with experience of adverse effects of conventional medications. From this cross-sectional analysis, complementary and alternative medicine use does not appear to be associated with reduced overall adherence to medical therapy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".