Googling Alzheimer Disease: An Infodemiological and Ecological Study
Bibliographic record
Abstract
INTRODUCTION: Understanding the emergent role of the internet on the health-seeking behavior of people is critical not only in the areas of medicine and public health but also in the field of infodemiology. METHODS: Using Google Trends, data on global search queries for Alzheimer disease (AD) between January 2004 and April 2021 were analyzed. The relationship between online interest, as reflected by search volume index (SVI), and measures of disease burden, namely prevalence, deaths, and disability-adjusted life years, was evaluated. RESULTS: There was a reduction in the tendency to search for AD during the past two decades. SVI peaks corresponded to news of famous people with AD and awareness months. Symptoms, causes, and differences with the term dementia were central queries for persons interested in AD. No notable overall correlation between SVI and measures of disease burden was found due to competing results. Sub-group analyses, however, showed that these correlations may be influenced by socioeconomic development, with strong negative significant associations observed in lower middle-income countries. CONCLUSION: Online interest in AD may represent a more complex metric influenced by socioeconomic factors. Awareness of the impact of celebrity diagnosis and awareness months on online search behavior may prove useful in the planning of public health campaigns for AD.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".