Needs and Experiences of Children and Adolescents with Pediatric Multiple Sclerosis and Their Caregivers: A Systematic Review
Bibliographic record
Abstract
In the present study we conduct a systematic review to evaluate the needs and experience of people with pediatric multiple sclerosis (MS) and their caregivers. The literature search was conducted across 10 academic databases, adhering to PRISMA-P guidelines. Quality appraisal was conducted using the mixed method appraisal test for individual studies, and GRADE-CERQual to establish overall confidence of findings. Results were analyzed using a process of narrative synthesis. We identified 26 studies which included 2253 children/adolescents with MS (CAMS) and 1608 caregivers. MS was reported to negatively impact experiences for CAMS in domains such as of school performance, social relationships, mental health, and overall physical functioning. Specifically, fatigue and social support were reported as the most important barriers and facilitators for CAMS, respectively. In terms of caregiver experience, negative impacts were reported on social functioning, mental health, and quality of life. Additionally, lack of awareness concerning MS was one of the biggest challenges reported. Caregivers expressed needs for psychological and social support. This study provides the first evidence regarding the needs and experiences of CAMS and their caregivers. Findings can be used to address policy gaps for supporting families affected by pediatric MS.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".