Dietary and herbal supplement consumer health information for pain: A cross-sectional survey and quality assessment of online content
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Patients are increasingly utilizing the internet to learn about dietary and herbal supplements (DHSs) for various diseases/conditions, including pain management. Online health information has been found to be inconsistent and of poor quality in prior studies, which may have detrimental effects on patient health. This study assessed the quality of online DHSs consumer health information for pain. Methods: Six search items related to DHSs and pain were used to generate the first 20 websites on Google across four English-speaking countries. The identified 480 webpages produced 68 eligible websites, which were then evaluated using the DISCERN tool. The mean scores and standard deviations (SD) of the reviewers' ratings on each of the 15 DISCERN instrument items as well as the overall total score were calculated. Results: The mean summed score for the 68 eligible websites was 46.6 (SD = 10.1), and the mean overall rating was 3.3 (SD = 0.8). Websites lacked information regarding areas of uncertainty, the effects of no treatment being used, and how treatments affect the overall quality of life. These shortcomings were especially apparent across commercial websites, which frequently displayed bias, failed to report the risks of DHS products, and lacked support for shared decision-making regarding the use of DHSs. Conclusion: Variability exists in the quality of online consumer health information regarding DHS use for pain. Healthcare providers should be aware of and provide guidance to patients regarding the identification of reliable online resources so that they can make informed decisions about DHS use for pain management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it