Prevention of Post-operative Delirium in the Elderly Using Pharmacological Agents
Bibliographic record
Abstract
INTRODUCTION: Post-operative delirium (POD) is a serious surgical complication that can cause significant morbidity and mortality. It is associated with prolonged hospital stay, delayed admission to rehabilitation programs, persistent cognitive deficits, marked health-care costs, and more. The pathophysiology is multi-factorial and not completely understood, which complicates the optimal management. Non-pharmacological measures have been the mainstay of treatment, but there has been an ongoing interest in the medical literature on the prevention of post-operative delirium using medications. The purpose of this review is to critically analyze the current evidence on pharmacological prevention of POD. METHODS: A literature review was conducted using PubMed and Embase databases, using the following search terms: delirium, anti-psychotics, cholinesterase inhibitors, and statins. RESULTS: A total of 1,152 articles were screened and 25 articles were reviewed. Fourteen articles found a reduced incidence of post-operative delirium using pharmacological agents: eight with antipsychotics, two with statins, one with melatonin, one with dexamethasone, one with gabapentin, and one with diazepam. However, study designs, methodological issues, or authors' interpretations raise questions on these conclusions. CONCLUSIONS: Further double-blinded randomized clinical trials should be conducted before administering pharmacological agents to reduce POD in a non-research setting.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".