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Record W4416935967 · doi:10.15829/1728-8800-2025-4612

Success analysis of virtual reality-based cognitive training in patients after coronary artery bypass grafting

2025· article· W4416935967 on OpenAlexaboutno aff
О. А. Трубникова, И. В. Тарасова, И. Н. Кухарева, А. S. Sosnina, T. B. Temnikova, Evgenia Gorbatovskaya

Bibliographic record

VenueCARDIOVASCULAR THERAPY AND PREVENTION · 2025
Typearticle
Language
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsnot available
FundersRussian Science Foundation
KeywordsNeurovascular bundlePerioperativeCognitionNeuropsychologyProspective cohort studyBypass graftingArteryPostoperative cognitive dysfunction

Abstract

fetched live from OpenAlex

Aim . To analyze the success of virtual reality-­based multitask cognitive training (VR-MCT) in patients who underwent on-pump coronary artery bypass grafting (CABG), based on an assessment of neuropsychological and neurochemical parameters. Material and methods . This prospective study included 49 male patients aged 45 to 75 years who underwent on-pump CABG and had early postoperative cognitive dysfunction (POCD). Beginning 3-4 days after CABG, patients underwent daily VR-MCT (mean session count — 6,7). In addition to the standard perioperative examination, all patients underwent psychometric testing and determination of neurovascular unit (NVU) markers — neuron-­specific enolase (NSE), S100β protein, and brain-­derived neurotrophic factor (BDNF). Results . The success rate of VR-MCT course was 43%; 21 of 49 patients did not show POCD according to the established criteria at 11-12 days after CABG. Patients with successful VR-MCT showed improvements in attention (p=0,034) and short-term memory (p=0,016) compared with patients with unsuccessful training in the early postoperative period. In patients with successful VR-MCT, peripheral blood BDNF levels before surgery (p=0,029) and 1-2 days after CABG (p=0,04) were significantly higher compared to patients with unsuccessful training. We established factors specifying the complex indicator of the neurodynamics domain in VR-MCT — educational level, intima-­media thickness, patient age, number of trainings and S100β protein level on day 1 after surgery (R 2 =0,38, F (5,43)=8,32, p<0,001); the attention domain — patient age, educational level, initial BDNF concentrations, both at the first day and on the first day. Peripheral blood S100β protein concentration and Montreal Cognitive Assessment (MoCA) scores were assessed (R 2 =0,52, F (6,42)=10,76, p<0,001); for the short-term memory domain, the patient's age and baseline BDNF, NSE, and glucose concentrations were assessed (R 2 =0,37, F (4,45)=10,15, p<0,001). Conclusion . The study results demonstrated that VR-MCT optimizes attention and short-term memory performance in patients with early POCD after on-pump CABG. Negative factors specifying cognitive status after VR-MCT include patient age, low education level, and ba­seline cognitive function, while protective factors include high pe­ri­operative BDNF concentrations and low peripheral blood con­cen­tra­tions of brain damage markers.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.276
Teacher spread0.255 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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