Early predictors of disability in paediatric multiple sclerosis: evidence from a multi-national registry
Bibliographic record
Abstract
BACKGROUND: Early recognition of markers of faster disability worsening in paediatric-onset multiple sclerosis (MS) is a key requisite of personalised therapy for children with MS at the earliest possible time. OBJECTIVE: To identify early predictors of rapid disability accrual in patients with paediatric-onset MS. METHODS: Using the global MSBase registry, we identified patients who were <18 years old at the onset of MS symptoms. The clinico-demographic characteristics examined as predictors of future MS Severity Score (MSSS) included sex, age at symptom onset, absence of disability at the initial assessment, maximum Expanded Disability Status Scale (EDSS) score, relapse frequency and presence of brainstem, pyramidal, visual or cerebellar symptoms in the first year. A Bayesian log-normal generalised linear mixed model adjusted for cumulative proportion of time on higher-efficacy disease-modifying therapies (DMTs) was used to analyse the data. RESULTS: 672 patients (70% female) contributing 9357 visits were included. The median age at symptom onset was 16 (quartiles 15-17) years. Older age at symptom onset (exp(β)=1.10 (95% CI 1.04 to 1.17)), higher EDSS score (1.22 (1.12 to 1.34)) and pyramidal (1.31 (1.11 to 1.55)), visual (1.25 (1.10 to 1.44)) or cerebellar (1.18 (1.01 to 1.38)) symptoms in the first year were associated with higher MSSS. MSSS was reduced by 4% for every 24% increase in the proportion of time on higher-efficacy DMTs (0.96 (0.93 to 0.99)). CONCLUSIONS: A relatively later onset of MS in childhood, higher disability and pyramidal, visual or cerebellar symptoms during the first year predicted significant worsening in disability in patients with paediatric-onset MS. Persistent treatment with higher-efficacy DMTs was associated with a reduced rate of disability worsening.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".