2 Neuropsychological Test Performance Following Acute COVID-19 Infection Recovery: A Case Control Study
Notice bibliographique
Résumé
Objective: Cognition has been identified as an area of priority in examining health impacts of COVID-19 infection, and evidence suggests the virus invades the brain, with potential for long-term cognitive impact. Studies utilizing screening measures have reported cognitive sequelae (e.g., attention disorder, executive dysfunction) of the post-COVID-19 condition (i.e., long-haulers). More extensive examination of cognitive difficulties via comprehensive neuropsychological assessment is critical to informing treatment for those experiencing cognitive or functional difficulties post-infection. We aimed to comprehensively evaluate cognitive resiliencies and vulnerabilities of acutely recovered COVID-19 patients, across key domains (i.e., attention, processing speed, language, visuospatial abilities, memory, executive functioning), compared to healthy controls. Participants and Methods: Adults (N=103; aged 19-85; 69.2% female) who had COVID-19 at least three months prior (n=50) and those with no history of infection (n=53) completed demographic and health questionnaires via Qualtrics, along with measures of depressive (CES-D) and anxiety (GAD-7) symptoms, the Lawton-Brody Instrumental Activities of Daily Living (IADL) Scale, and a measure of subjective cognitive difficulties (SCD-Q). Participants (n=84) completed a teleneuropsychology assessment including a short interview and battery of neuropsychological tests assessing attention (BTA, Digit Span Forward), processing speed (DKEFS Colour Naming & Word Reading, SDMT), language (FAS, Animals, NAB Naming), visuospatial abilities (JLO, RCFT Copy), verbal and visual memory (HVLT-R, NAB Shape Learning, RCFT), and executive function (DKEFS Color-Word Interference & Switching, Digit Span Backward & Sequencing, BRIEF), and including multiple measures of cognitive effort/assessment validity (RFIT, RDS), and a self-report measure of symptom validity (SIMS). T-tests were used to examine demographic and health variables between COVID-19 and control groups. MANCOVA were used to examine group differences across each cognitive domain assessed, and across cognitive effort and symptom validity tasks, while controlling for English language status. Results: Group comparisons indicated that the COVID-19 group was slightly older (mean age = 40 vs. 34 yrs.; f=-2.101, p=0.04). Those who had COVID-19 reported more difficulties completing IADLs (f=2.204; p=0.03), more depressive symptoms (f=-2.299; p=0.02), and more subjective cognitive difficulties (f=-3.886; p<0.01). Examination of cognitive performance indicated a main effect of prior infection on executive function, controlling for language status (Wilks’ /\=0.817, F(6,73)=2.733, p=0.02). Specifically, having COVID-19 was associated with worse DKEFS Colour-Word Switching performance (p=0.01) and slightly higher selfreported difficulties on the BRIEF MI (p=0.04). No other significant group differences were seen across cognitive domains. There was also a main effect of COVID-19 infection on effort and symptom validity task performance (Wilks’ /\=0.705, F(10,70)=2.923, p<0.01). Specifically, prior infection was associated with higher SIMS Neurologic Impairment (p<0.01) and Amnestic Disorders (p<0.01) subscale scores, and paradoxically, slightly higher RFIT combined scores (p=0.02). Conclusions: Interestingly, results indicate a significant role for subjective cognitive complaints and potential exaggeration of cognitive symptoms post-COVID-19 infection, in the absence of differences in objective performance in most cognitive domains. While subtle differences are seen on some executive function measures, mean group differences are small, and in the context of higher SIMS subscale scores, may not be readily interpretable. Studies employing similarly comprehensive neuropsychological assessments including validity measures in larger samples are needed to further disambiguate potential objective cognitive performance decrements from subjectively experienced difficulties.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».