45 A systematic review of cognitive correlates of fatigue in pediatric-onset multiple sclerosis
Notice bibliographique
Résumé
Objective: Fatigue is common in pediatric-onset multiple sclerosis (POMS), yet causal factors and correlates of fatigue are poorly understood in this population. A 2016 review suggested an association between fatigue and emotional difficulties, sleep disturbance, and reduced quality of life in POMS. Information regarding the potential association between fatigue and cognitive challenges is limited and mixed. Through this systematic review, we searched for relationships between fatigue, cognition, and mental health. Participants and Methods: Systematic review methodology and PRISMA guidelines were followed. Five electronic databases were searched: Ovid: Medline, Ovid: EMBASE, Ovid: PsycInfo, Web of Science and CINAHL. Search terms were specific to each database. Reference lists of included studies were also hand-searched. We included empirical studies that were published in English after 2001, included a sample with confirmed diagnoses of POMS using McDonald criteria, and measured fatigue, cognition and clinical factors including mental health outcomes. Cognition had to be assessed using a standardized assessment tool and studies must have examined associations between outcomes of interest either descriptively or by assessing bivariate or multivariate relationships. Covidence was used to complete the screening, extraction, and quality assessment. Two independent researchers (i.e., T.L.F, and/or S.D, and/or M.G) reviewed each paper included in the title and abstract screen and full text review. S.D and M.G completed the extraction and quality assessments. Conflicts at all stages were resolved by the lead author (T.L.F). The University of Adelaide JBI critical appraisal checklist for analytical cross-sectional studies was used to ensure the scientific rigor of each included study. Sample characteristics and measures of fatigue, clinical and cognitive variables were extracted. A narrative synthesis was conducted. Results: We identified 1025 abstracts through our initial search and retained 119 articles for full text review. One hundred and six of these studies were excluded during the full text review including six studies which did not examine the relationship between the outcomes of interest. Fifty-one additional studies were identified from hand-searching reference lists of included studies, of which 24 were retained for full text review. A total of 15 studies were extracted and analyzed. Overall, a positive relationship was found between fatigue and mental health outcomes (i.e., anxiety and depression), whereas results were mixed regarding the association between fatigue and performance-based measures of cognition as well as fatigue and other clinical characteristics (e.g., disease duration, EDSS, treatment with DMDs, relapse rate, age at disease onset). In some studies, fatigue and executive functioning performance were negatively related; the relationship was less clear in others (e.g., both fatigued and non-fatigued MS patients demonstrated cognitive challenges, an association between fatigue and executive functioning was identified at follow-up but not baseline). Eleven of the 15 included studies (73%) did not identify associations between fatigue and cognition. Conclusions: While studies are mixed, fatigue in children has been associated with aspects of cognition. Understanding the relationship between fatigue, cognition, and mental health and identifying gaps in the existing literature, have implications for informing interventions for this clinical population.
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 | 0,014 | 0,063 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,012 | 0,011 |
| Bibliométrie | 0,016 | 0,016 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 ».