Motivations behind complementary and alternative medicine use in patients with Crohn’s disease and ulcerative colitis
Bibliographic record
Abstract
Abstract Background Complementary and alternative medicine (CAM) use is common in inflammatory bowel disease (IBD) patients and impacts compliance with conventional treatment. Gastroenterologists should understand the motivational factors of CAM use—factors that push patients away from standard therapy or pull towards CAM. Our study describes the motivations behind CAM use for IBD and evaluates differences between Crohn’s disease (CD) and ulcerative colitis (UC) patients. Methods Retrospective cohort survey of patients over 18 years old with IBD, evaluated by gastroenterologists at a tertiary care referral centre from January 1 to December 31, 2019. Only patients who reported CAM use were included. Chi-square and independent t-tests were performed and P-value <0.05 was significant. Results Of the 230 completed surveys, 193 reported CAM use (CD: 57.5% vs UC: 42.5%). Demographics, disease duration, and hospitalizations were similar, but CD patients had lower SIBDQ scores (CD: 48.1 vs UC: 53.5, P < 0.001). Both groups were largely influenced by their social network to use CAM (CD: 33% vs UC: 31.3%) and did not feel well informed about CAM (87.4%). CD and UC patients had similar push and pull factors. Push factors included lack of improvement (39%) and side effects (20%) with conventional treatment. Pull factors included the desire for a holistic approach (21%) and to improve mood (35%). UC patients wanted a natural approach to treat their IBD, which nearly reached significance (P = 0.049). Most patients hoped fatigue 62.7%, and diarrhoea 61.7% would improve with CAM, but more CD patients wanted to improve their appetite (P = 0.043). Conclusion Despite differences in QoL, push and pull motivations for CAM use did not differ between CD and UC patients. Most users do not feel well informed of CAM and ongoing dialogue is important for patient-centred care.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".