Use of Complementary and Alternative Medicine in Children with Asthma
Bibliographic record
Abstract
BACKGROUND: Because of the potential risk of interaction with, and underuse of, conventional medications, it is important to document the prevalence of the use of complementary and alternative medicines (CAMs) in asthmatic children. OBJECTIVE: To ascertain the prevalence and type of CAMs, and to identify factors associated with their use. METHODS: A cross-sectional survey of children who presented to the Asthma Centre of The Montreal Children's Hospital (Montreal, Quebec) between 1999 and 2007 was conducted. At the initial consultation, parents completed a questionnaire inquiring, in part, about CAM use. Computerized health records provided information regarding patient characteristics and their condition. RESULTS: The median age of the 2027 children surveyed was 6.1 years (interquartile range 3.3 to 10.5 years); 58% were male and 59% of children had persistent asthma. The prevalence of CAM use was 13% (95% CI 12% to 15%). Supplemental vitamins (24%), homeopathy (18%) and acupuncture (11%) were the most commonly reported CAMs. Multivariable logistic regression analysis confirmed the association of CAM use with age younger than six years (OR 1.86; 95% CI 1.20 to 2.96), Asian ethnicity (OR 1.89; 95% CI 1.01 to 3.52), episodic asthma (OR 1.88; 95% CI 1.08 to 3.28) and poor asthma control (OR 1.98; 95% CI 1.80 to 3.31). CONCLUSION: The prevalence of reported CAM use among Quebec children with asthma remained modest (13%), with vitamins, homeopathy and acupuncture being the most popular modalities. CAM use was associated with preschool age, Asian ethnicity, episodic asthma and poor 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".