Coronary and Cerebrovascular Events and Exacerbation of Existing Conditions After Laboratory‐Confirmed Influenza Infection Among US Veterans: A Self‐Controlled Case Series Study
Bibliographic record
Abstract
BACKGROUND: Influenza may contribute to coronary/cerebrovascular events and exacerbate underlying conditions. METHODS: We used self-controlled case series (SCCS) design to analyze data from US Veterans ≥18 years with coronary/cerebrovascular or exacerbation event +/-1 year of lab-confirmed influenza (LCI) during 2010-2018. We estimated the incidence ratio (IR) (95% CI) of the event for risk interval (Days 1-7 post-LCI) versus control interval (all other times +/-1 year of LCI) with fixed-effects conditional Poisson regression. We included biomarker data for mediation analysis. RESULTS: We identified 3439 episodes with coronary/cerebrovascular-related hospitalizations. IRs (95% CI) for LCI risk versus control interval were STEMI 0.6 (0.1, 4.4), NSTEMI 7.3 (5.8, 9.2), ischemic stroke 4.0 (3.0, 5.4), hemorrhagic stroke 6.2 (3.4, 11.5), and coronary spasm 1.3 (0.5, 3.0). IR significantly increased for NSTEMI and ischemic stroke among those ≥ 65 years. IR for NSTEMI and ischemic stroke dropped 26% and 10%, respectively, when white blood cell (WBC) and platelet count were considered. LCI was significantly associated with exacerbation of preexisting asthma, chronic obstructive pulmonary disease, and congestive heart failure. CONCLUSIONS: We found significant association between LCI and hospitalization for NSTEMI, ischemic stroke, and hemorrhagic stroke, the latter possibly due to unaccounted time-varying confounding in SCCS design.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".