The effect of statins on influenza‐like illness morbidity and mortality
Bibliographic record
Abstract
PURPOSE: The effect of statins on cytokine-mediated inflammatory responses may impact on the prognosis of influenza. We assessed whether statin use decreases the incidence of adverse influenza-related outcomes. Additionally, we used a new-user study design to minimize healthy user bias. We further examined the possibility of non-causal associations by using unrelated outcomes. METHODS: We used the UK Clinical Practice Research Datalink to identify all patients aged 30 or older diagnosed with influenza-like illness during 1997-2010. Statin users were compared with propensity score-matched patients not receiving statins. The outcome was hospitalization for influenza or pneumonia or death in the 30 days following influenza diagnosis. Logistic regression estimated cumulative incidence ratios. RESULTS: The study cohort included 5181 statin users matched to 5181 non-users. The 30-day incidence of hospitalization or death was 3.5% in statin users and 5.2% in non-users, resulting in a 27% lower incidence with statin use (cumulative incidence ratio: 0.73, 95%CI: 0.59-0.89). New statin users were less protected against our composite outcome. The effect of statins was less pronounced among those with respiratory and cardiac disease. Statin use was shown to be associated with a non-statistically significant risk reduction of motor vehicle accident and burns. CONCLUSION: The attenuation of the effect of statins with the new-user design, supporting evidence from the assessment of effect modification, and additional sub-analyses evaluating the effect of statins on non-related outcomes suggest that the beneficial effect of statins on influenza-related adverse outcomes may be explained by a healthy user bias. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".