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Record W7117261342 · doi:10.1002/alz70857_099172

What is the prevalence of impaired cognitive domains in surgical patients? A systematic review and meta‐analysis

2025· article· en· W7117261342 on OpenAlexaff
Subin Park, Ellene Yan, Ray Martinez‐Rodriguez, Bijal Desai, Jonathan Chung, Aparna Saripella, Marina Englesakis, Keera N. Fishman, Frances Chung

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsBaycrest HospitalOntario Shores Centre for Mental Health SciencesToronto Western HospitalUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsCognitionCognitive declineCognitive impairmentCognitive disabilitiesMEDLINESelection (genetic algorithm)Cognitive test

Abstract

fetched live from OpenAlex

BACKGROUND: Cognitive impairment is prevalent in the surgical population but remains significantly under-recognized. This systematic review and meta-analysis aims to (1) assess the prevalence of specific impaired cognitive domains in surgical patients perioperatively and (2) examine postoperative changes across the domains. METHODS: A comprehensive search was conducted in five electronic databases from inception until March 19, 2024. Studies were included if they met the following criteria: (1) surgical patients ≥18 years of age; (2) sample size of ≥100 surgical patients; (3) assessed cognitive domain(s) preoperatively with a neuropsychological battery or clinical evaluation; and (4) reported the prevalence of impaired cognitive domains or changes perioperatively. The exclusion criteria included neurosurgery; those with overlapping data; cross-sectional, case control, and case series studies; and non-English articles. RESULTS: Of the 12,082 articles screened, 21 studies with 5,725 patients were included (11 non-cardiac surgery, 10 cardiac surgery). Preoperatively, executive function had the highest pooled prevalence of impairment (18%; 95% CI: 13%, 24%), followed by visuospatial function (16%; 95% CI: 6%, 26%) and attention/working memory/processing speed (14%, 95% CI: 9%, 18%). Rates were lower in perceptual-motor control (13%; 95% CI: 9%, 36%), language (13%; 95% CI: 8%, 17%), and learning/memory (12%; 95% CI: 8%, 16%) (Figure 1). Postoperatively, impairment was most pronounced at one week, with 35% (95% CI: 4%, 66%) in attention/working memory/processing speed, 34% (95% CI: 16%, 51%) in executive function, and 28% (95% CI: 16%, 40%) in learning/memory. Impairment reduced at three months postoperatively, with a prevalence of 16% (95% CI: 3%, 35%) in attention/working memory/processing speed, 15% (95% CI: 6%, 24%) in executive function, and 12% (95% CI: -2%, 25%) in learning/memory (Figure 2). CONCLUSIONS: Cognitive domains were impaired preoperatively in 12% to 18% of surgical patients. The most commonly impaired domains were executive function, visuospatial function, and attention/working memory/processing speed. One-week post-surgery, the prevalence of impaired cognitive domains rose to 28% to 35%, then decreased by three months to levels similar to preoperative rates (12% to 16%). Identifying commonly impaired cognitive domains can inform the selection of cognitive screening tools to assess surgical patients at greater risk for adverse outcomes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.041
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0180.038
Bibliometrics0.0080.009
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.308
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designMeta-analysis
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

Citations0
Published2025
Admission routes1
Has abstractyes

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