The Use of Complementary and Alternative Medicine in Children with Chronic Medical Conditions
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to examine whether progressive medical conditions lead to greater use of complementary and alternative medicine (CAM) as compared with more stable conditions, to see whether disease severity influences CAM use, and to identify the main motivations behind CAM use. METHODS: Subjects were selected from outpatient clinics at Hotel Dieu Hospital. Surveys were conducted by mail and telephone. Medical diagnosis and severity were obtained from medical files. Statistical tests included chi, Kruskal-Wallis, and correlations. RESULTS: One hundred ninety-four children were surveyed. The "progressive" group included 15 patients with Duchenne muscular dystrophy and 22 patients with cystic fibrosis. The "nonprogressive" group included 85 patients with cerebral palsy (CP), 49 with diabetes mellitus, and 23 with spina bifida. Twenty-three percent were using CAM. CP had the highest use; diabetes mellitus had the lowest. Popular therapies included massage and dietary/herbal remedies. Progressiveness had no impact on CAM use. Within the CP group, greater disease severity was associated with higher use (P < 0.001). The main reason for CAM use was to complement conventional medicine. CONCLUSIONS: Disease progressiveness had no impact on CAM use, but severity within the CP group did. Complementing conventional medicine was the main motive. Understanding the reasons and patterns of use of CAM is beneficial in efforts to improve the care of children with chronic medical conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".