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Record W4416693161 · doi:10.5327/cbn240754

Evaluation of cognitive impairment in long-severe COVID patients

2024· article· W4416693161 on OpenAlexaboutno aff
Leidys Marina Pedrozo García, Marciéli Gerhardt, Fábio Jean Varella de Oliveira, William Alves Martins, Eduardo Leal‐Conceição, Helena Scartassini Erwig, Larissa Alves Fernandes, Helena Morsch Marques, Jaderson Costa da Costa

Bibliographic record

VenueArquivos de Neuro-Psiquiatria · 2024
Typearticle
Language
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionQuality of life (healthcare)Montreal Cognitive AssessmentCognitive impairmentSocial isolationExecutive functionsMental healthCognitive deficit

Abstract

fetched live from OpenAlex

Background: “Long Covid” is an expression for describing the syndrome with signs and symptoms that emerge or continue after a Covid-19 acute infection. These symptoms must persist for over 12 weeks and are not explained by an alternative diagnosis. Persistent symptoms include breathing difficulty, fatigue, mental health disorders and cognitive impairment. Objective: To measure and characterize the cognitive impairment and psychiatric disorders related to Covid-19 infection. Methods: This is a cross-sectional study, approved by our institution’s ethics committee (CEP:6.123.125), in a population-based cohort. Two hundred patients admitted to the ICU of a hospital in Porto Alegre due to severe Covid-19 infection and who were discharged from 01/15/2021 to 03/30/2021 were assessed for eligibility. Data regarding cognitive function (Montreal Cognitive Assessment [MoCA] and Addenbrooke’s Cognitive Examination [ACE-III]), psychiatric comorbidities (Hamilton scales) and quality of life (Short Form Health Survey [SF-36]) were collected. Results: A total of 46 patients were enrolled, regardless of their cognitive status, with an average age of 52.2 years (DP 12.1). Twenty-four (54,3%) were male. The vast majority (95.6%) was not experiencing social isolation and had at least eleven years of education (91.3%). We found that many patients suffered from executive and visuospatial dysfunction, performing similarly in MoCA and ACE-III, with a median score of 3.87 (maximum score of 5; DP 1.29; P=0.099) and 14 (maximum score of 16; DP 1.08), respectively. Language was also impaired in our patients, with average scores of 2.52 (maximum score of 3; DP 0.72; P=0.380) and 24.82 (maximum score of 26; DP 1.82) in MoCa and ACE-III, respectively. Regarding memory, MoCA had an average score of 2.7 (maximum score of 5; DP 1.48), while ACE-III showed a score of 18.7 (maximum score of 26; DP 1.5). Unexpectedly, memory performance in both tests was significantly worse even in patients with more than 14 years of formal education. Of all patients, 30,5% (n=14) had normal cognition, 13.04% (n=6) had physical deficit without cognitive impairment, 34.78% (n=16) had mild cognitive impairment (MCI) and 21.73% (n=10) had dementia. The median scores in MoCA were 27.36 (DP±1,216), 27.67 (DP±0,816), 22.75 (DP±2,543) and 22.30 (DP±3,129) in each group, respectively. The median scores in ACE-III were 87.79 (DP±5,820), 92.33 (DP±5,502), 83.81 (DP±8,101) and 80.2 (DP±7,642), respectively. Conclusion: This study provides evidence for a protracted cognitive impairment years after severe Covid-19 infection, showing a combination of executive, visuospatial, language and memory impairments. Although other factors, such as prolonged ICU stay, may have had a role, our findings support that even relatively heathy patients suffer from Covid-19 cognitive dysfunction, which was displayed even more prominently in highly educated patients.

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)0.001
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.338
Teacher spread0.317 · 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
Published2024
Admission routes1
Has abstractyes

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