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Analysis of neuropsychological and laboratory parameters in patients with cerebrovascular disease and SARS-CoV-2 compared to those without SARS-CoV-2

2024· article· en· W4393009712 on OpenAlexaboutno aff
V.V. Marshtupa, T.I. Nasonova

Bibliographic record

VenueINTERNATIONAL NEUROLOGICAL JOURNAL · 2024
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsnot available
Fundersnot available
KeywordsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)Medicine2019-20 coronavirus outbreakNeuropsychologyPandemicSars virusDiseaseVirologyPsychiatryInternal medicineInfectious disease (medical specialty)CognitionOutbreak

Abstract

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Background. Severe acute respiratory syndrome сoronavіrus 2 (SARS-CoV-2, formerly known as 2019-nCoV) is the cause of coronavirus disease 2019 (COVID-19), and was first reported in Wuhan, China. However, it is also contagious to humans and spreads rapidly around the world through close contact between infected people or through a relatively simple transmission mechanism (airborne transmission). COVID-19 is known to affect almost all systems of the human body. Initial reports suggest that hypertension may be a risk factor for susceptibility to SARS-CoV-2 infection, a more severe course of COVID-19, and increased mortality associated with COVID-19. It is estimated that 1–3 % of COVID-19 patients experience transient ischemic attacks with a frequency similar to other coronavirus infections (SARS-CoV-1 and MERS-CoV). The cause of ischemic stroke associated with COVID-19 is unknown, but previous studies have suggested that an inflammatory cytokine storm may cause hypercoagulation and endothelial damage. We see that COVID-19 is closely related to neurological complications because there are potential factors that can cause them. Materials and methods. Cerebrovascular diseases were analyzed in 111 patients infected with SARS-CoV-2 (n = 71) and those without a history of SARS-CoV-2 (n = 40). The subject of the study was neuropsychological and laboratory indicators. The following methods were used: psychometric — Beck Anxiety Inventory, Hamilton Depression Rating Scale, Fatigue Assessment Scale; neuropsychological — Mini-Mental State Examination, Montreal Cognitive Assessment, Frontal Assessment Battery; clinical — neurological status; polymerase chain reaction to detect COVID-19 RNA; statistical methods. Results. In patients who suffered transient ischemic attack and ischemic stroke with a minimal neurological deficit and COVID-19, there were elevations in the erythrocyte sedimentation rate, leukocytes, segmented neutrophils, while an increase in C-reactive protein was noted in all participants with cerebrovascular disease and COVID-19, with more significant levels among those with ischemic stroke. All subgroups with COVID-19 showed an increase in D-dimer and fibrinogen with higher content in patients after ischemic stroke. Also in this subgroup, the procalcitonin index exceeded the norm, which indicates the severity of the course of COVID-19 with the addition of co-infection. Data of neuropsychological tests in patients with ischemic stroke with a minimal neurological deficit with SARS-CoV-2 revealed a decrease in the Montreal Cognitive Assessment score, indicating mild cognitive changes in these patients. The level of anxiety in patients with hypertension with frequent crises and ischemic stroke with a minimal neurological deficit was above the reference values, with a slight predominance in patients who did not have COVID-19. It follows that both laboratory and neuropsychological parameters differed in three subgroups depending on cerebrovascular disease, as well as the presence and absence of SARS-CoV-2, which makes it possible to develop more appropriate diagnostic methods in order to predict the course and outcome of COVID-19.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.024
GPT teacher head0.332
Teacher spread0.308 · 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.

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
Published2024
Admission routes1
Has abstractyes

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