Perthes Disease
Bibliographic record
Abstract
BACKGROUND: Research has shown that up to 89% of parents used the Internet to seek health information regarding their child's medical condition. Much of the information on the Internet is valuable; however, the quality of health information is variable and unregulated. The aim of this study was to evaluate the quality and content of information about Perthes disease on the Internet using recognized scoring systems, identification of quality markers, and describe a novel specific score. METHODS: We searched the top 3 search engines (Google, Yahoo!, and Bing) for the following keywords: "Perthes disease." Forty-five unique Web sites were identified. The Web sites were then categorized by type and assessed using the DISCERN score, the Journal of the American Medical Association (JAMA) benchmark criteria, and a novel Perthes-specific Content score. The presence of the Health On the Net (HON) code, a reported quality assurance marker, was noted. RESULTS: Of the Web sites analyzed, the Majority were Governmental and Nonprofit Organizations (NPO) (37.8%), followed by commercial Web sites (22.2%). Only 6 of the Web sites were HONcode certified. The mean DISCERN score was 53.1 (SD=9.0). The Governmental and NPO Web sites had the highest overall DISCERN scores followed closely by Physician Web sites. The mean JAMA benchmark criteria score was 2.1 (SD=1.2). Nine Web sites had maximal scores and the Academic Web sites had the highest overall JAMA benchmark scores. DISCERN scores, JAMA benchmark scores, and Perthes-specific Content scores were all greater for Web sites that bore the HONcode seal. CONCLUSIONS: The quality of information available online regarding Perthes disease is of variable quality. Governmental and NPO Web sites predominate and also provide higher quality content. The HONcode seal is a reliable indicator of Web site quality. CLINICAL RELEVANCE: Physicians should recommend the HONcode seal to their patients as a reliable indicator of Web site quality or, better yet, refer patients to sites they have personally reviewed. Supplying parents with a guide to health information on the Internet will help exclude Web sites as sources of misinformation.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".