Use of complementary and alternative medicine in patients with inborn errors of metabolism: A single‐center study
Bibliographic record
Abstract
Abstract Background and Objectives There is a paucity of information on the use of complementary and alternative medicine (CAM) in patients with inborn errors of metabolism (IEM). This study's objective was to evaluate the self‐reported use and perceived effectiveness of CAM in adults and children with IEM. Methods Patients aged 0‐70 years and caregivers seen at the London Health Sciences Centre Metabolic Clinic (London, Ontario, Canada) between July 2017 and August 2017 were recruited to complete a questionnaire regarding CAM use to help their IEM diagnosis and perceived effectiveness of these therapies. Survey responses were analyzed using descriptive statistics; age, sex, and education level associations among CAM users were tested using the Pearson χ2 test. Results Of 50 potential participants, 44 (88%) completed the questionnaire, including 21 adults (6 by caregivers) and 23 children (22 by caregivers). The most common IEM category was Aminoacidopathies and Small Molecule Disorders (50%). Twenty‐seven (61%) participants reported CAM use to help their IEM diagnosis. The most common CAM therapies used were chiropractic manipulation, omega‐3 fatty acids, probiotics, and aromatherapy/essential oils. Most CAM users and caregivers (74%) perceived their CAM therapies as effective overall. Among CAM users, 40% had not discussed CAM use with a health care professional (HCP). CAM use was similar when comparing age, sex and education level. Conclusions CAM is commonly used among patients with IEM. The safety and efficacy of CAM therapies for IEM should be further investigated. HCPs and patients should openly discuss CAM use in order to evaluate safety.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".