Relationship between Supra-Annual Trends in Influenza Rates and Stroke Occurrence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Stroke occurrence appears to be a random event, yet annual and supra-annual periodicity is observed. Recent attention in atherosclerotic disease etiology has focused on infectious and inflammatory mechanisms. Influenza is one such infection that may influence stroke occurrence. METHODS: We explored population-based time series data on stroke occurrence and influenza activity. Using Fourier transformation to isolate low-frequency signals in the data, the inverse transformed time series were regressed using Prais-Winsten regression to correct for serially auto-correlated residuals, to assess the relationship between influenza rates and stroke occurrence rates. RESULTS: Changes in the low-frequency components of influenza activity predicted the changes in low-frequency components of the stroke occurrence data with a delay of about 20 weeks. The delay between changes in influenza activity and subsequent stroke activity was different for different stroke types. Overall, the effect size was small with a tripling of the influenza rate associated with about a 6% change in stroke occurrence rate. CONCLUSIONS: A small proportion of the patterns of stroke occurrence may be explained by variation in influenza activity. Further evaluation of influenza as a triggering agent in stroke is needed.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it