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Record W4281564808 · doi:10.15690/vramn1650

Study of Anxiety and Depression Factors in People with Mild Cognitive Impairment in COVID-19 Pandemic

2022· article· en· W4281564808 on OpenAlexaboutno aff
Olga Karpenko, Timur Syunyakov, Natalia G. Osipova, Viktor B. Savilov, M V Kurmyshev, G. P. Kostyuk

Bibliographic record

VenueAnnals of the Russian academy of medical sciences · 2022
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyPandemicContext (archaeology)Montreal Cognitive AssessmentPsychologyHospital Anxiety and Depression ScaleDepression (economics)Clinical psychologyStressorCognitionPsychiatryObservational studyMental healthMedicineGerontologyCoronavirus disease 2019 (COVID-19)Cognitive impairmentDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Background. The COVID-19 pandemic is a major stressor with predictable negative impacts on mental health, especially for vulnerable populations, which include older people. Emotional disorders, a decrease in intellectual, physical, social activity are the risk factors for the development of cognitive decline in older people; in the situation of the COVID-19 pandemic, the influence of all these factors is exacerbated. In this regard, it seems relevant to study the level of emotional disorders and factors affecting the emotional state of patients with mild cognitive impairment (MCI) in the context of the COVID-19 pandemic in comparison with the period before the pandemic. Aims: emotional state assessment in patients over 55 years old with MCI during the COVID-19 pandemic and identification of factors influencing the emotional state of these patients. Materials and methods: A cross-sectional single-center observational study of patients with MCI who applied to the Memory Clinic in the autumn of 2018 (n = 121), 2019 (n = 114), in the autumn of 2020 (n = 70), and in the spring of 2020 (n = 110). Patients were examined using the Hospital Anxiety and Depression Scale (HADS), the Montreal Cognitive Assessment (MoCA), the MiniMental State Examination (MMSE), and the Khachinsky Modified Ischemia Assessment Scale. In 2020, in addition to these scales, a questionnaire Personal experience of COVID-19 pandemic was applied to assess the experience associated with the new coronavirus infection. Results: The severity of emotional disorders, assessed by HADS scale, did not differ between groups (F = 0.751; p = 0.522 and F = 0.310; p = 0.818 for the HADS anxiety and depression subscales, respectively). Adjustment for covariates (scores on the Khachinsky and/or MoCA and/or MMSE scales) did not affect the significance of differences between groups on the HADS subscales, regardless of the correction for multiple comparisons. Pathway modeling analysis demonstrated the low ability of the models to predict emotional state based on risk factors (age, gender, Khachinsky score) and cognitive symptoms (MoCA and MMSE scores) all coefficients r 0.7. A change in intellectual activity (decrease) and subjective impression of the difficulties obtaining medical care were associated with a higher score on the HADS anxiety scale. Decreased physical health and decreased personal communication were associated with higher scores on the HADS depression scale. Clinically pronounced changes in the emotional state were noted only in relation to anxiety, which depended on the changes in intellectual activity. Conclusions: severity of anxiety and depression was not increased in patients with MCI, regardless of the control of additional factors. No differences were found in the contribution of risk factors (age, gender, vascular and atrophic factors of cognitive decline) and cognitive dysfunction to the formation of emotional disorders in comparing with previous years.

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)0.001
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.078
GPT teacher head0.404
Teacher spread0.326 · 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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Citations2
Published2022
Admission routes1
Has abstractyes

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