P.049 Seasonal variations in aneurysmal subarachnoid hemorrhage: revisiting the myth using google trends
Bibliographic record
Abstract
Background: Results of previous studies examining seasonal variation in the incidence of aneurysmal subarachnoid hemorrhage (SAH) are conflicting. The aim of this study is to investigate whether there is a seasonal effect in online search queries for SAH that may reflect an association between meteorological factors and aneurysm rupture. Methods: We utilized the Google Trends data service to analyze the volume of internet queries for SAH on Google’s search engine from January 1, 2004 to November 2016. We used comprehensive search terms and collected data from: USA, Canada, Finland, and Japan, as well as worldwide search volume. Potential seasonal variations in the data were assessed by comparative non-parametric tests and curve-fit regression model. Results: Our analyses revealed that USA had the highest median search scores (115 vs. 86, 46, 46 for Finland, Canada and Japan, respectively). The term “brain aneurysm” was the commonly used search term among countries, followed by “cerebral aneurysm”. There was no evidence of seasonality in any of the countries studied on both univariate tests and regression time-adjusted analysis. Conclusions: There are no seasonal variations in internet search query volume for SAH. Further studies are needed to explore whether online search volumes correlate with the actual incidence of SAH.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| 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".