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Record W4414217441 · doi:10.17816/clinpract654808

Risk factors for post-operative cognitive dysfunction in neurosurgical patients

2025· article· en· W4414217441 on OpenAlexaboutno aff
В.А. Салтанова, О. А. Кичерова, Л. И. Рейхерт, Yu.I. Doyan, Nikita A. Mazurov

Bibliographic record

VenueJournal of clinical practice · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsNeurosurgeryCognitionBody mass indexDiabetes mellitusIntervention (counseling)Risk factorAnesthetic

Abstract

fetched live from OpenAlex

BACKGROUND: The impact of various risk factors on the development of post-operative cognitive dysfunction in neurosurgical patients requires research for decreasing the probability of developing this complication. AIM: To determine the effects of extra- and intraoperative risk factors on the development of post-operative cognitive dysfunction in neurosurgical patients after undergoing a vertebral column surgery with long-running anesthetic support. METHODS: The research was carried out among the neurosurgical patients with previous surgical intervention in the vertebral column, within the premises of the Neurosurgery Department of the State Budgetary Healthcare Institution of the Tyumen Oblast “Regional Clinical Hospital No. 2”. The evaluation included the cognitive functions before surgery and on Day 3 after the surgical intervention using the Montreal Cognitive Assessment (MoCA), along with a panel of Isaac tests and the Munsterberg test. The calculated coefficients were the Pearson’s and the point biserial correlation coefficients regarding the following intraoperative risk factors: type and duration of anesthetic management, medications used for anesthesia and muscle relaxation, as well as the type of surgery. Evaluations were also made for the interrelation between the development of post-operative cognitive dysfunction and the following extra-operational risk factors: the age, the body mass index, the number of education years, the presence of arterial hypertension or diabetes and smoking. RESULTS: A notable positive correlation was observed between the development of post-operative cognitive dysfunction and the age (r=0.53; p 0.01), moderate correlation with the body mass index (r=0.35; p 0.01) and with the presence of arterial hypertension (r=0.42; p 0.05). A moderate negative relation was observed for the number of education years and the development of post-operative cognitive dysfunction (r=-0.36; p 0.01). The relation of the presence of diabetes with post-operative cognitive dysfunction did not show significant correlation. Smoking and surgery duration show low level of interrelation, which does not allow to comprehensively interpret the obtained results as significant. The type of surgical intervention and the duration of anesthetic support did not correlate with the development of post-operative cognitive dysfunction (r 0.1; p 0.01). A moderate correlation was found for the anesthesia conducting using a drug combination of desflurane+fentanyl (r=0.31; p 0.05) along with the mild one when combining sevoflurane+fentanyl+ketamine (r=0.25; p 0.05). The usage of fentanyl together with sevoflurane (r=0.07), propofol (r=-0.1) and sodium oxybutyrate (r=0.05) does not lead to post-operative cognitive dysfunction (p 0.05). CONCLUSION: Elderly age, high body mass index, presence of arterial hypertension and low education level increase the risks of developing post-operative cognitive dysfunction. Using the desflurane+fentanyl and sevoflurane+fentanyl+ketamine combinations can also contribute to the occurrence of cognitive disorders.

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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.442
Teacher spread0.400 · 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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