Analysis of the quality of meningioma education resources available on the Internet
Bibliographic record
Abstract
BACKGROUND: Meningiomas are the most common primary central nervous system tumors and patients face difficulty evaluating resources available online. The purpose of this study is to systematically evaluate the educational resources available for patients seeking meningioma information on the Internet. METHODS: A total of 127 meningioma websites were identified by inputting the term "meningioma" on Google and two meta-search engines. A structured rating tool developed by our research group was applied to top 100 websites to evaluate with respect to accountability, interactivity, readability, and content quality. Responses to general and personal patient questions were evaluated for promptness, accuracy, and completeness. The frequency of various social media account types was analyzed. RESULTS: Of 100 websites, only 38% disclosed authorship, and 32% cited sources. Sixty-two percent did not state date of creation or modification, and 32% provided last update less than 2 years ago. Websites most often discussed the definition (99%), symptoms (97%), and treatment (96%). Prevention (8%) and prognosis (47%) were most often not covered. Only 3% of websites demonstrated recommended reading level for general population. Of 84 websites contacted, 42 responded, 32 within 1 day. CONCLUSIONS: Meningioma information is readily available online, but quality varies. Sites often lack markers for accountability, and content may be difficult to comprehend. Information on specific topics are often not available for patients. Physicians can direct meningioma patients to appropriate reliable online resources depicted in this study. Furthermore, future web developers can address the current gaps to design reliable online resources.
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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 | 0.004 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".