Do antipsychotics prevent postoperative delirium? A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To summarize the effect of antipsychotics for preventing postoperative delirium. DESIGN: We conducted a literature search using Medline, EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews and clinicaltrials.gov. We included randomized controlled trials of adults undergoing surgery who were given antipsychotics to prevent postoperative delirium. Quality was assessed via the Cochrane risk of bias tool. Random-effects meta-analysis and meta-regression were conducted. Q-statistics and I(2) were used for assessment of heterogeneity. The main outcome was delirium incidence using validated definitions. RESULTS: A total of 1710 subjects were included, with a mean age ranging from 60.7 to 86.4 years. Antipsychotics reduced the incidence of postoperative delirium with the global effect-size estimate (weighted odds ratio) using the random effects model of 0.44 (95% confidence interval: 0.28-0.70; N = 6; Q-value: 16, p-value 0.0005; I(2) = 69%). Significant heterogeneity existed with the pooled global effect of delirium incidence; however, meta-regression allowed us to test both treatment-level and patient-level explanations for significant between-study variance. Baseline risk for delirium was found to be a significant contributor to study heterogeneity, and meta-regression suggested that antipsychotic type and dosage were two of the several treatment-level factors that also may have led to heterogeneity. Our analysis implied the presence of a breakeven baseline level of delirium risk below which preventive treatment with antipsychotics might prove ineffective. CONCLUSIONS: Within the limits of few randomized controlled trials, antipsychotics appeared to reduce the incidence of postoperative delirium in several surgical settings, predominantly orthopedic and for those at higher risk for delirium.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".