Pharmacologic Interventions to Prevent Delirium in Trauma Patients: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
To compare the relative efficacy of pharmacologic interventions in the prevention of delirium in ICU trauma patients. DATA SOURCES: We searched Medical Literature Analysis and Retrieval System Online, Embase, and Cochrane Registry of Clinical Trials from database inception until June 7, 2022. We included randomized controlled trials comparing pharmacologic interventions in critically ill trauma patients. STUDY SELECTION: Two reviewers independently screened studies for eligibility, extracted data, and assessed risk of bias. DATA EXTRACTION: Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines for network analysis were followed. Random-effects models were fit using a Bayesian approach to network meta-analysis. Between-group comparisons were estimated using hazard ratios (HRs) for dichotomous outcomes and mean differences for continuous outcomes, each with 95% credible intervals. Treatment rankings were estimated for each outcome in the form of surface under the cumulative ranking curve values. DATA SYNTHESIS: = 382 patients) were included. Compared with combined propofol-dexmedetomidine, there may be no difference in delirium prevalence with dexmedetomidine (HR 1.44, 95% CI 0.39-6.94), propofol (HR 2.38, 95% CI 0.68-11.36), nor haloperidol (HR 3.38, 95% CI 0.65-21.79); compared with dexmedetomidine alone, there may be no effect with propofol (HR 1.66, 95% CI 0.79-3.69) nor haloperidol (HR 2.30, 95% CI 0.88-6.61). CONCLUSIONS: The results of this network meta-analysis suggest that there is no difference found between pharmacologic interventions on delirium occurrence, length of ICU stay, length of hospital stay, or mortality, in trauma ICU 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 | 0.028 | 0.074 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.039 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".