Evaluation of Chronic Pain-Related Information Available to Consumers on the Internet
Bibliographic record
Abstract
OBJECTIVE: Recent surveys suggest more than one third of patients utilize the Internet to seek information about chronic pain (CP) and that 60% of patients feel more confident in the information provided online than provided by their physician. Unfortunately, the quality of online information is questionable. For example, some Websites make unsubstantiated claims while others may have covert motives (i.e., product advertisement). This article presents two studies that utilized a well-validated tool to evaluate the quality of online CP-related information. DESIGN: A Website search was conducted by entering the most commonly used pain-related search terms into the three most commonly used search engines in North America. In study 1, the first 50 Websites from each search were evaluated using a consumer-focused evaluation tool-the DISCERN. In study 2, 21 clients with CP used the DISCERN to rate a random selection of Websites from among the 10 highest scoring and five lower scoring sites from Study 1, and answered open-ended questions regarding the DISCERN and Websites. RESULTS: Ratings indicated that Websites ranged substantially in quality, with many providing incomplete and incorrect information, and others providing accurate and detailed information. The majority of the Websites provided low-quality information. Client ratings of the Websites were consistent with those of the researchers. CONCLUSIONS: Overall, these findings speak to the risks associated with clients making CP-related treatment choices based on information obtained online without first evaluating the Website.
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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 | 0.063 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.048 | 0.007 |
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; both teacher heads agree on what is shown here.
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".