Established Statin Use Reduces Mortality From Community-Acquired Pneumonia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Statin therapy (ST) has been associated with improved outcomes from sepsis. Our objective was to systematically review the association between established ST and outcomes of patients with community-acquired pneumonia (CAP) that is severe enough to require hospitalisation. Methods: Two meta-analyses were conducted following a search of articles published before 31st January 2013. After exclusions, seven studies were included to assess the effects of statins on 30-day mortality from CAP, and eight studies were included to assess the effects of statins on the development of CAP. Endpoints were a reduction in the risk of 30-day mortality or risk of developing CAP. Results: A reduction in the risk of 30-day mortality from CAP was identified in patients established on ST (pooled odds ratio [OR]: 0.70, 95% confidence interval [CI]: 0.65-0.76; adjusted OR: 0.58, 95% CI: 0.47-0.69). The pooled OR for risk of developing CAP in patients with and without established ST was 1.01 (95% CI: 0.98-1.04). Conclusion: There appears to be weak evidence to suggest a potential benefit of established ST. It is associated with a reduced risk of 30-day mortality in patients subsequently hospitalised with CAP. Further evidence is required, but ST could be considered as a means of reducing the risk of mortality from pneumonia.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.028 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".