Pharmacotherapeutic approaches for the effective treatment of postoperative delirium: the state of play
Bibliographic record
Abstract
INTRODUCTION: Post-operative delirium (POD) is a common neuropsychiatric complication that is associated with increased morbidity, prolonged hospital stays, and persistent cognitive deficits. Despite its clinical relevance, pharmacologic treatment options remain limited and inconsistently supported by evidence. AREAS COVERED: In this review, the authors synthesize the current understanding of pathophysiological mechanisms underlying POD then critically evaluate the evidence around pharmacotherapeutic interventions, focusing on the use of antipsychotics, cholinesterase inhibitors, sleep-wake cycle modulators, and dexmedetomidine, in the treatment of established delirium. Relevant articles were identified using PubMed, EMBASE, and Cochrane databases. EXPERT OPINION: There remains insufficient evidence to support the routine use of pharmacologic interventions in the treatment of POD. While the evidence supporting dexmedetomidine seems most promising, its clinical significance is questionable and therefore its impact is likely in prevention rather than treatment. Various methodological challenges, including heterogeneity in trial design and insufficient stratification by delirium subtype, hinder generalizability of study results and advancements in how delirium is understood. Future progress will depend on reconceptualizing delirium away from a purely descriptive condition and toward a heterogenous, biologically driven disorder.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".