Peaks in online inquiries into pharyngitis-related symptoms correspond with annual incidence rates
Bibliographic record
Abstract
OBJECTIVE: To assess whether web-based public inquiries into pharyngitis-related search terms follow annual incidence peaks of acute pharyngitis in various countries from both hemispheres. METHODS: Google Trends (GT) was utilized for systematic acquisition of pharyngitis-related search terms (sore throat, cough, fever, cold). Six countries from both hemispheres including four English (United Kingdom, United States, Canada, and Australia) and two non-English speaking countries (Austria and Germany) were selected for further analysis. Time series data on relative search interest for pharyngitis-related search terms, covering a timeframe between 2004 and 2019 were extracted. Following reliability analysis using the intra-class correlation coefficient, the cosinor time series analysis was utilized to determine annual peaks in public-inquiries. RESULTS: The extracted datasets of GT proved to be highly reliable with correlation coefficients ranging from 0.83 to 1.0. Graphical visualization showed annual seasonal peaks for pharyngitis-related search terms in all included countries. The cosinor time series analysis revealed these peaks to be statistically significant during winter months (all p < 0.001). CONCLUSION: Our study revealed seasonal variations for pharyngitis-related terms which corresponded to winter incidence peaks of acute pharyngitis. These results highlight the need for easily accessible information on diagnosis, therapy, and red-flag symptoms for this common disease. Accurately informed patients might contribute to a reduction of unnecessary clinic visits and potentially cutback the futile antibiotic overuse.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".