Complementary and alternative medicine use in children and adolescents with type 1 diabetes
Bibliographic record
Abstract
BACKGROUND: The use of complementary and alternative medicine (CAM) in paediatric patients varies between 11% and 68%. There are limited reports of its use in children with type 1 diabetes mellitus (T1DM). OBJECTIVE: To describe the use of CAM in children with T1DM, and the perceptions of both users and nonusers regarding the effect of CAM on diabetes management. DESIGN/METHODS: A cross-sectional, anonymous questionnaire survey was mailed to a randomly selected subgroup of patients with T1DM. Each patient's main caregiver was asked to complete the questionnaire. RESULTS: Of 403 questionnaires mailed, 195 (48%) were completed. The mean (± SD) age of the children was 12.2±4.0 years (56% boys). Use of CAM was reported in 110 children (56%) (vitamins/minerals [n=99], herbal medicine [n=22], dietary supplement [n=13]). When excluding the use of vitamins/minerals, the CAM number dropped to 47 children (24%). Only the current age of the child was significantly different between users and nonusers of CAM. In users, reasons cited for using CAM were to minimize symptoms, improve control, prevent complications and add benefits to insulin. Only 30% of CAM users stated that CAM improved diabetes control. Nonusers cited satisfaction with current diabetes treatment and lack of knowledge as reasons for not using CAM. CONCLUSIONS: CAM use in children with T1DM was frequent, and appeared to be an attempt to improve control or prevent diabetes complications. However, improved control was not reported as a benefit. Diabetes care teams should assess the use of CAM in children with T1DM, and monitor for any potential positive or negative effects.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".