Effect of atorvastatin on delirium status of patients in the intensive care unit: a randomized controlled trial
Bibliographic record
Abstract
Introduction Delirium is one of the most prevalent complications in intensive care unit (ICU) patients, which is related to worse clinical outcomes including a longer ICU stay, longer duration of mechanical ventilation, higher mortality rates and increased risk of cognitive impairment. Observational studies have suggested that statins might have a positive effect on delirium status of hospitalized patients. To date, there has been no trial assessing the effect of atorvastatin on delirium status in critically ill patients. Thus, the aim of the current study was to determine the efficacy of atorvastatin on delirium status of patients in the ICU. Material and methods In this randomized, double-blind and controlled trial, a total of 90 patients in the general ICU who had delirium for at least 2 days were randomly divided into atorvastatin (40 mg/day) (n = 40) and control (n = 50) groups. Delirium status of the patients was determined twice a day at 10:00 a.m. and 18:00 p.m. using the Richmond Agitation-Sedation Scale (RASS). Results Administration 40 mg/day of atorvastatin significantly reduced the mean RASS score and increased delirium-free days at both morning and afternoon time points compared to the control group (p < 0.05). Conclusions Administration of atorvastatin had a significant positive effect on delirium status in patients admitted to the ICU.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".