Complementary, Holistic, and Integrative Medicine: Utilization Surveys of the Pediatric Literature
Bibliographic record
Abstract
1. Soleil Surette, MLIS* 2. Sunita Vohra, MD, MSc*,† 1. *CARE Program, University of Alberta, Edmonton, Alberta, Canada. 2. †Edmonton General Hospital, University of Alberta, Edmonton, Alberta, Canada. * Abbreviations: ASD: : autism spectrum disorder CAM: : complementary and alternative medicine Whether you believe in the effectiveness of complementary and alternative medicine (CAM) or not, some of your patients and their parents do. As you read this article, please note the number of patients who use CAM. Joseph A. Zenel, MD Editor-in-Chief Health care professionals do not ask consistently about complementary, holistic, and integrative medicine use by their patients, yet it is important to do so because patients and families often pursue this course of therapy for specific medical conditions and do not volunteer this information. No thorough assessment has been made of the literature on the use of pediatric complementary and alternative medicine (CAM) since 1999, when Ernst published a systematic review on this topic. (1) As part of its horizon scanning, the Complementary and Alternative Research and Education (CARE) program (www.care.ualberta.ca) tracks the use of CAM in the pediatric literature and, as of March 2011, has identified 160 English-language studies dating back to 1982. Utilization literature can be a valuable source of information for determining what CAM practices and products warrant further pediatric research. This article explores the 5 most studied pediatric specialty populations: oncology, asthma, autism spectrum disorder (ASD), gastrointestinal diseases (eg, inflammatory bowel disease and irritable bowel syndrome), and pediatric emergency care. The Table lists these studies, with references. View this table: Table. Articles on the 5 Most Studied Pediatric …
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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 | 0.016 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".