Statin potency and the risk of hospitalization for community‐acquired pneumonia
Bibliographic record
Abstract
AIM: Previous studies suggest that statins may have beneficial respiratory effects. However, it is unclear if these purported benefits vary with statin potency. Our objective was to determine if higher potency statins, compared with lower potency statins, were associated with a reduced risk of hospitalization for community-acquired pneumonia (HCAP). METHODS: We conducted a nested case-control analysis of a retrospective, population-based cohort of new users of statins using data extracted from the UK's Clinical Practice Research Datalink and Hospital Episode Statistics. For each HCAP case, we used risk set sampling to randomly select up to 10 controls, matched on sex, age, cohort entry date and follow-up duration. We used conditional logistic regression with high-dimensional propensity scores to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for HCAP with current use of higher potency statin vs. lower potency statins. RESULTS: A total of 217 721 patients entered the cohort on a lower potency statin and 130 707 entered on a higher potency statin; these patients resulted in 2251 cases of HCAP during 561 886 person-years of observation (rate: 4.0 HCAP per 1000 persons per year, 95% CI: 3.8-4.2). The analysis included 22 178 matched controls. Compared with lower potency statins, higher potency statins were associated with an increased rate of HCAP (HR: 1.14, 95% CI: 1.03-1.27). Higher potency statins were also associated with an increased rate of fatal HCAP (HR: 1.29, 95% CI: 1.04-1.59). CONCLUSIONS: Higher potency statins were not associated with a decreased risk of HCAP compared with lower potency statins.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".