Promotion and marketing of bioidentical hormone therapy on the internet: a content analysis of websites
Bibliographic record
Abstract
OBJECTIVE: To evaluate the quality of information presented and claims made on websites offering bioidentical hormone therapy (BHT) products or services. METHODS: A quantitative content analysis was completed on 100 websites promoting or offering BHT products or services. Websites were identified through Google search engine from September to October 2013. Search terms included "bioidentical hormone therapy" or "bioidentical progesterone," accompanied by "purchase or buy," "service," or "doctors." The Brief DISCERN instrument was used to determine the quality of the health information. RESULTS: Websites were from Canada (59%), United States (38%), and other countries (3%). Almost half of the websites originated from medical clinics (47%), and healthcare professionals offering BHT services included physicians (50%), pharmacists (19%), and naturopaths (16%). Majority of websites promoted BHT as custom-compounded formulations (62%), with only 27% indicating that BHT is also commercially available. Websites overall claimed that BHT had less risk compared with conventional hormone therapy (62%). BHT was described as having less breast cancer risk (40%), whereas over a quarter of websites described BHT as "protective" for breast cancer. Websites mainly targeted women (99%), with males mentioned in 62% of websites. Product descriptors used to promote BHT included individualization (77%), natural (70%), hormone imbalance (56%), and antiaging (50%). The mean Brief DISCERN score was 15, indicating lower quality of information. CONCLUSIONS: Claims made about BHT on the internet are misleading and not consistent with current professional organizations' recommendations. Understanding how BHT may be promoted on the internet can help healthcare professionals when educating 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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".