Image_13_Association of Preadmission Statin Use and Mortality in Critically Ill Patients: A Meta-Analysis of Cohort Studies.TIF
Bibliographic record
Abstract
<p>Background: A large number of studies have been conducted to determine whether there is an association between preadmission statin use and improvement in outcomes following critical illness, but the conclusions are quite inconsistent. Therefore, this meta-analysis aims to include the present relevant PSM researches to examine the association of preadmission use of statins with the mortality of critically ill patients.</p><p>Methods: The PubMed, Web of Science, Embase electronic databases, and printed resources were searched for English articles published before March 6, 2020 on the association between preadmission statin use and mortality in critically ill patients. The included articles were analyzed in RevMan 5.3. The Newcastle-Ottawa Scale (NOS) was used to conduct quality evaluation, and random/fixed effects modeling was used to calculate the pooled ORs and 95% CIs. We also conducted subgroup analysis by outcome indicators (30-, 90-day, hospital mortality).</p><p>Results: All six PSM observational studies were assessed as having a low risk of bias according to the NOS. For primary outcome—overall mortality, the pooled OR (preadmission statins use vs. no use) across the six included studies was 0.86 (95% CI, 0.76–0.97; P = 0.02). For secondary outcome—use of mechanical ventilation, the pooled OR was 0.94 (95% CI, 0.91–0.97; P = 0.0005). The corresponding pooled ORs were 0.67 (95% CI, 0.43–1.05; P = 0.08), 0.91 (95% CI, 0.83–1.01; P = 0.07), and 0.86 (95% CI, 0.83–0.89; P < 0.00001) for 30-, 90-day, and hospital mortality, respectively.</p><p>Conclusions: Preadmission statin use is associated with beneficial outcomes in critical ill patients, indicating a lower short-term mortality, less use of mechanical ventilation, and an improvement in hospital survival. Further high-quality original studies or more scientific methods are needed to draw a definitive conclusion.</p>
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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 | 0.000 | 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) | 0.456 | 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".