Surfing for mouth guards: assessing quality of online information
Bibliographic record
Abstract
INTRODUCTION: The Internet is an easily accessible and commonly used source of health-related information, but evaluations of the quality of this information within the dental trauma field are still lacking. AIM: The aims of this study are (i) to present the most current scientific knowledge regarding mouth guards used in sport activities, (ii) to suggest a scoring system to evaluate the quality of information pertaining to mouth guard protection related to World Wide Web sites and (iii) to employ this scoring system when seeking reliable mouth guard-related websites. MATERIALS AND METHODS: First, an Internet search using the keywords 'athletic injuries/prevention and control' and 'mouth protector' or 'mouth guards' in English was performed on PubMed, Cochrane, SvedMed+ and Web of Science to identify scientific knowledge about mouth guards. Second, an Internet search using the keywords 'consumer health information Internet', 'Internet information public health' and 'web usage-seeking behaviour' was performed on PubMed and Web of Science to obtain scientific articles seeking to evaluate the quality of health information on the Web. Based on the articles found in the second search, two scoring systems were selected. Then, an Internet search using the keywords 'mouth protector', 'mouth guards' and 'gum shields' in English was performed on the search engines Google, MSN and Yahoo. The websites selected were evaluated for reliability and accuracy. RESULTS: Of the 223 websites retrieved, 39 were designated valid and evaluated. Nine sites scored 22 or higher. The mean total score of the 39 websites was 14.2. Fourteen websites scored higher than the mean total score, and 25 websites scored less. The highest total score, presented by a Public Institution Web site (Health Canada), was 31 from a maximum possible score of 34, and the lowest score was 0. CONCLUSION: This study shows that there is a high amount of information about mouth guards on the Internet but that the quality of this information varies. It should be the responsibility of health care professionals to suggest and provide reliable Internet URL addresses to patients. In addition, an appropriate search terminology and search strategy should be made available to persons who want to search beyond the recommended sites.
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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 | 0.013 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".