Complementary and alternative asthma treatments and their association with asthma control: a population-based study
Bibliographic record
Abstract
OBJECTIVES: Many patients with asthma spend time and resources consuming complementary and alternative medicines (CAMs). This study explores whether CAM utilisation is associated with asthma control and the intake of asthma controller medications. DESIGN: Population-based, prospective cross-sectional study. SETTING: General population residing in two census areas in the province of British Columbia, Canada. Recruitment was based on random-digit dialling of both landlines and cell phones. PARTICIPANTS: 486 patients with self-reported physician diagnosis of asthma (mean age 52 years; 67.3% woman). PRIMARY AND SECONDARY OUTCOME MEASURES: We assessed CAM use over the previous 12 months, level of asthma control as defined by the Global Initiative for Asthma and the self-reported intake of controller medications. Multivariate logistic regression was performed to study the relationship between any usage of CAMs (outcome), asthma control and controller medication usage, adjusted for potential confounders. RESULTS: A total of 179 (36.8%) of the sample reported CAM usage in the past 12 months. Breathing exercises (17.7%), herbal medicines (10.1%) and vitamins (9.7%) were the most popular CAMs reported. After adjustment, female sex (OR 1.66; 95% CI 1.09 to 2.52) and uncontrolled asthma (vs controlled asthma, OR 2.25, 95% CI 1.30 to 3.89) were associated with a higher likelihood of using any CAMs in the past 12 months. Controller medication use was not associated with CAM usage in general and in the subgroups defined by asthma control. CONCLUSIONS: Clinicians and policy makers need to be aware of the high prevalence of CAM use in patients with asthma and its association with lack of asthma control.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".