45 A systematic review of cognitive correlates of fatigue in pediatric-onset multiple sclerosis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".