Efficacy of Natural Health Products in Treating Osteoporosis: What is the Quality of Internet Patient Advice?
Bibliographic record
Abstract
BACKGROUND: With Canadians increasing their use of the Internet to find health-related information, especially regarding natural health products (NHPs), there is a need for high-quality, evidence-based information on Web sites to aid consumers in making informed decisions regarding the appropriate and safe use of NHPs. OBJECTIVE: To determine the quality of Web sites that target consumers and advocate the use of NHPs in the management of osteoporosis in postmenopausal women. METHODS: Web sites were identified via the Google search engine using the key words "natural treatment osteoporosis." The first pages of the first 91 Web sites identified were assessed for relevance based on the following criteria: (1) written in English, (2) contained consumer information, and (3) claimed a benefit of a single NHP in the management of osteoporosis. This task was completed by 2 investigators; differences were resolved by consensus after discussion with the third investigator. Quality of relevant sites was assessed using an expanded DISCERN instrument that also examined the evidence supporting the claim of benefit. Additionally, readability of the sites was assessed. RESULTS: Thirty-eight Web sites met the inclusion criteria. Using the DISCERN instrument, we found that many of the sites scored low, suggesting serious or extensive shortcomings. On many Web sites, benefit claims regarding calcium, vitamin D, phytoestrogens, dehydroepiandrosterone and vitamin K were consistent with empirical evidence. However, for other NHPs, many of these same sites made effectiveness claims that were not supported by current evidence from randomized controlled trials. Twenty-five sites did not provide information as to what resources were used to support their claims. The average reading grade score was grade 11.9 (based on US school grades) and the mean Flesch Reading Ease Score was 41.7. (A higher score out of 100 indicates ease of reading.) CONCLUSIONS: Due to the poor quality and content from unknown sources found on some Web sites, consumers who access Web sites for information regarding the use of NHPs in osteoporosis should do so cautiously and discuss results with their healthcare providers.
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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 | 0.052 | 0.282 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".