Web-based online resources about adverse interactions or side effects associated with complementary and alternative medicine: a systematic review, summarization and quality assessment
Bibliographic record
Abstract
BACKGROUND: Given an increased global prevalence of complementary and alternative medicine (CAM) use, healthcare providers commonly seek CAM-related health information online. Numerous online resources containing CAM-specific information exist, many of which are readily available/accessible, containing information shareable with their patients. To the authors' knowledge, no study has summarized nor assessed the quality of content contained within these online resources for at least a decade, specifically pertaining to information about adverse effects or interactions. METHODS: This study provides summaries of web-based online resources that provide safety information on potential interactions or adverse effects of CAM. Specifically, clinicians are the intended users of these online resources containing patient information which they can then disseminate to their patients. All online resources were assessed for content quality using the validated rating tool, DISCERN. RESULTS: Of 21 articles identified in our previously published scoping review, 23 online resources were eligible. DISCERN assessments suggests that online resources containing CAM-specific information vary in quality. Summed DISCERN scores had a mean of 56.13 (SD = 10.25) out of 75. Online resources with the highest total DISCERN scores across all questions included Micromedex (68.50), Merck Manual (67.50) and Drugs.com (66.50). Online resources with the lowest total scores included Drug Information (33.00), Caremark Drug Interactions (42.50) and HIV Drug Interactions (43.00). The DISCERN questions that received the highest mean score across all online resources referred to whether the risks were described for each treatment (4.66), whether the aims were clear (4.58), whether the source achieved those aims (4.58), and whether the website referred to areas of uncertainty (4.58). The DISCERN questions that received the lowest mean score across all online resources assessed whether there was discussion about no treatment being used (1.29) and how treatment choices would affect quality of life (2.00). CONCLUSION: This study provides a comprehensive list of online resources containing CAM-specific information. Informed by the appraisal of these resources, this study provides a summarized list of high quality, evidence-based, online resources about CAM and CAM-related adverse effects. This list of recommended resources can thereby serve as a useful reference for clinicians, researchers, and patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".