Bibliographic record
Abstract
OBJECTIVE: To systematically review the evidence evaluating the role of statin therapy in sepsis. DATA SOURCES: MEDLINE, EMBASE, and PubMed were searched (1980-January 2007) for English-language clinical trials that evaluated the use of statins and the development and treatment of sepsis in human subjects. Search terms included statin, HMG-CoA reductase inhibitor, bacteremia, sepsis, septic shock, septicemia, and severe sepsis. In addition, pertinent references from identified articles were obtained. STUDY SELECTION AND DATA EXTRACTION: Only clinical trials with primary efficacy outcomes of mortality, incidence of sepsis, and severe sepsis were included. DATA SYNTHESIS: Seven retrospective and 2 prospective cohort studies were included in this review. One was excluded because the patient population was not experiencing sepsis. Three studies demonstrated a reduced mortality with statin use while 2 other studies did not demonstrate this mortality benefit. One study suggested increased mortality with statin use in sepsis. Three studies showed a reduced incidence of development of sepsis or sepsis-related outcomes, while one study did not. The observational and retrospective nature of these studies and the higher rate of cardiovascular comorbidities in the statin groups may have allowed for a confounding influence. The conflicting results and heterogeneity between the studies makes the observed association between statin use and incidence of sepsis and sepsis-related mortality inconclusive. The clinical benefit of statin therapy in sepsis remains to be determined. CONCLUSIONS: There is an association between statin use and a lower incidence of sepsis and sepsis-related mortality. However, a causal relationship between statin use and reduced sepsis-related mortality has not yet been established. Currently, statins cannot be recommended for sepsis prevention or treatment until controlled trials are performed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".