An Evaluation of Internet Sites for Burn Scar Management
Bibliographic record
Abstract
Patients rely on the Internet for medical information. It is difficult to discern which resources are accurate or appropriate for patients. The purpose of this study was to develop a quality-assessment tool for health Internet Web sites and to apply this tool to assess the quality of burn scar management information on the Internet. Between September and December 2001, we prospectively evaluated all Web sites on the Internet search engine Yahoo! containing the headings "burn scar management," "burn scar healing," "burn scar treatment," and "burn scar therapy." The quality of each Web site's medical information was evaluated using our scoring system consisting of the following two components: quality and technical characteristics. The total score for each Web site was converted to a percent grade (eg, > or =80% = excellent, 70 to 79% = very good, 60 to 69% = good, 50 to 59% = fair, and <50% = poor). The Web sites were grouped into three categories: commercial (for profit), academic (university, hospital), and organizational (nonprofit). Of 88 Web sites evaluated, the majority 68 (77%) were commercial, 7 (8%) academic, and 13 (15%) organizational. Burn scar management information on the Internet was poor in the majority (79%) of commercial Web sites and was excellent, very good, or good in the majority of academic (86%) and organizational (77%) Web sites. Using our health information evaluation, we found that the majority of burn scar management information on the internet (77%) was of fair or poor quality. Academic and organizational Web sites had the best quality of burn scar management information. Additional testing of the developed tool will be needed to analyze the reproducibility of the results and their applicability in other medical domains.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".