Infodemiology of autoimmune encephalitis: An analysis of online search behavior using Google Trends
Bibliographic record
Abstract
Abstract BackgroundPatients and their caregivers, including clinicians and educators, use web-based search engines to access healthcare-related information from the internet. Online search behavior analysis has been used to obtain insights on health information demand.ObjectivesWe aimed to describe the online search behavior for autoimmune encephalitis worldwide over time through the analysis of search volumes made on Google.MethodsIn this infodemiological study, we retrieved search volume indices for the keyword “autoimmune encephalitis (disease)” based on worldwide search data from January 01, 2004 to October 31, 2021, using Google Trends. We performed a descriptive analysis of search volume patterns, including related topics and queries.ResultsThere was a progressive increase in search volume numbers over time for the keyword “autoimmune encephalitis (disease)” with no annual seasonal variation. Peak search volume was recorded in July 2018. The greatest search volume was recorded in Singapore, followed by Australia, the United States of America, the Philippines, and New Zealand. The most searched topics were related to autoimmune encephalitis definition, causes, symptoms, diagnosis, and treatment. All related topics and queries increased in volume by more than 5000-fold over time.ConclusionsThis study showed an uptrend in the online search interest on autoimmune encephalitis over time, which may reflect the increased awareness on the condition by the public and the medical community. Information on online health information-seeking behavior may be obtained from Google Trends data despite its limitations.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".