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Record W4412161284 · doi:10.1080/14737175.2025.2532075

Pharmacotherapeutic approaches for the effective treatment of postoperative delirium: the state of play

2025· review· en· W4412161284 on OpenAlexaff
Eric Toyota, Verinder Sharma

Bibliographic record

VenueExpert Review of Neurotherapeutics · 2025
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsSt Joseph's Health CareWestern University
Fundersnot available
KeywordsDeliriumMedicineIntensive care medicineAnesthesia

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.920
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.096
GPT teacher head0.419
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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