Predictors of Alternative and Complementary Medicine Use in Inflammatory Bowel Disease: Do Measures of Conventional Health Care Utilization Relate to Use?
Bibliographic record
Abstract
OBJECTIVE: Alternative and complementary therapies (ACM) have gained increasing attention in the past few years. It was our purpose to determine whether increased ACM use is associated with increased use of conventional health care resources. Additionally, demographics of use, subjective benefit, and cost were analyzed. METHODS: We enrolled 150 inflammatory bowel disease (IBD) patients from a tertiary care center and performed a phone survey of their ACM use in the past year. A population-based administrative database was accessed to extract data regarding use of conventional medicine (hospitalizations, doctor visits, and GI specific doctor visits). Patients were divided into three groups: (i) no ACM (n = 60) (ii) users of exercise, diet, and prayer (EDP) exclusively (n = 47) (iii) other ACM use (n = 43) which included those who may have used EDP as well as any of acupuncture, chiropractic, homeopathy, naturopathy, herbology, massage, relaxation, reflexology, hypnotherapy, aromatherapy, meditation, or support group. RESULTS: ACM was used by 60% (EDP 31%, other ACM 29%). There were no significant differences in use between the three groups by disease diagnosis, education level, employment status, use of IBD medications, number of hospitalizations, doctor visits, or GI specific doctor visits. The EDP group was more likely to be married (p = 0.006) and female (p = 0.04) compared to no ACM. The EDP group tended to be older than the no ACM (p = 0.001) and other ACM (p = 0.01). The other ACM had shorter disease duration than EDP (p = 0.04) and no ACM (p = 0.04). The most commonly used therapies were diet (45%), herbal (17%), exercise (15%), prayer (11%), and relaxation (10%). ACM was sought for pain/cramps (64%), diarrhea (60%), and gas/bloating (21%). Seventy-three percent of EDP interventions incurred no cost compared to 33% with other ACM (p < 0.0001). The median annual amount spent on other ACM was $56 (range $0-$4800). Subjectively, patients felt helped by trials of EDP 95% of the time whereas other ACM helped 67% of the time (p < 0.0001). CONCLUSIONS: ACM use could not be predicted by either greater or less hospitalizations, conventional doctor visits, or GI specific visits. ACM was sought mostly to palliate pain or diarrhea. Those using EDP are more likely to be older married women. Subjectively other ACM is of less benefit (67%) than EDP (95%). If doctor visits or hospitalizations represent degree of increased disease activity then this too is not predictive of using ACM.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".