50 Pain severity as a predictor of verbal fluency functioning after COVID-19 illness
Notice bibliographique
Résumé
Objective: Published results focusing on language assessment in acutely recovered COVID-19 patients have shown communication problems in this group, including significant cognitive-linguistic disruptions in verbal fluency (Cummings, 2022). Extant research also indicates that poorer health-related outcomes, such as reduced physical functioning and quality of life, co-occur with cognitive difficulties post-COVID-19 infection (Mendez et al., 2021; Tabacof et al., 2022). Understanding what factors may worsen the impact of COVID-19 on cognition, and aspects of language function specifically, is necessary to determine who is at greatest risk of adverse outcomes following infection. Our goal was to examine the effect of health-related outcomes on language abilities, specifically verbal fluency, post-COVID-19 infection. Participants and Methods: 37 adults 19 years and older (M age = 38.78, 67.5% female, 92.5%> high school education) were recruited from British Columbia and Ontario, Canada. Participants provided documentation indicating they had had a COVID-19 infection at least 3 months prior to participation. Participants completed a series of online questionnaires, including the Short Form Health Survey (SF-20), to measure aspects of health-related quality of life. The SF-20 measures dimensions of functioning (physical, social, role) and well-being (mental health, health perception, pain). For each parameter except pain, higher scores indicate better functioning/well-being; for pain higher scores indicate greater pain levels. Participants also completed neuropsychological tests, including measures of verbal fluency, via teleconference. Animals and F-A-S total scores were combined to represent verbal fluency (semantic and phonemic, respectively) performance. To assess the impact of health outcomes on verbal fluency performance, hierarchical regression analyses were conducted. The six SF-20 subscale scores were entered as predictors and verbal fluency score (sum) as the outcome. Age and sex (Male/Female) were controlled for in the model. Results: Age and sex were not significantly related to verbal fluency scores in our sample. After controlling for these demographics, the overall model including SF-20 subscales did not significantly predict fluency performance (F (8, 28) = 1.04, p = .433). However, Pain scores did individually predict verbal fluency performance (B = 5.60, t = 2.53, p = <.05). Unexpectedly, pain ratings were positively associated with fluency scores, such that each increase in pain rating (e.g., “none” to “mild”) was associated with a fluency score increase of 5.60 points (i.e., 5.6 more words stated across administered tasks). Conclusions: These preliminary findings suggest that participants’ self-reported pain severity was positively associated with verbal fluency task performance in our sample (i.e., greater pain severity predicting better fluency). These findings are contrary to substantial evidence showing the deleterious effects of pain on cognitive functions in other populations (Khera & Rangasamy, 2021). It is possible that findings may be explained by a potential unknown intervening variable not included in our model. This is the first study to our knowledge to examine associations between experienced pain and verbal fluency performance post-COVID-19 infection. It will be important for future work to not only utilize more robust measures of pain experiences and explore more areas of cognition and language, but also to employ larger samples and examine a broader set of covariates.
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 | 0,000 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».