Use of statins and the risk of delirium in critically ill and surgical patients
Bibliographic record
Abstract
BACKGROUND: The critically ill and surgical patients are at significant risk of delirium, which is associated with a high morbidity and mortality. The association between statin use and the incidence of delirium is still controversial. In this article, we will perform a systematic review and meta-analysis of published studies to evaluate the effectiveness of statins for the prophylaxis of delirium among critically ill and surgical patients. METHODS: We will conduct a systematic literature search in EMBASE, PubMed, and the Cochrane Library from inception date to October 2018 for randomized controlled trials (RCTs) and observational studies (either cohort or case-control studies) investigating the association between use of statins and delirium risk. The Cochrane Collaboration's tool for evaluating the risk of bias and Newcastle-Ottawa scale (NOS) will be used to assess the methodological quality of RCTs and observational studies, separately. The primary outcome will be the risk of incident delirium associated with statin use. Pooled odds ratios (ORs) with corresponding 95% confidence intervals (CIs) will be calculated by a random-effects or fixed-effects model according to heterogeneity among included studies. Subgroup analyses, meta-regression method, and assessment of publication bias will be also performed. Statistical analyses will be conducted with RevMan (version 5.3.5) and Stata (version 14.0) software. In addition, the grading of recommendations assessment, development and evaluation (GRADE) approach will be applied to evaluate the quality of evidence. RESULTS: The study will provide a high-quality synthesis and evaluate the effectiveness of statins for delirium prevention among critically ill and surgical patients. CONCLUSIONS: The systematic review and meta-analysis will provide convincing evidence concerning the effect of statins against delirium in critically ill and surgical patients.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".