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.
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.
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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".