MRI in the Prediction and Diagnosis of Pediatric-onset Multiple Sclerosis: Insights from Children with Incident CNS Demyelination
Bibliographic record
Abstract
An acute demyelinating syndrome (ADS) in a child may be a monophasic illness or may represent the incident attack of multiple sclerosis (MS) – an inflammatory demyelinating neurodegenerative disorder affecting the brain, spinal cord and optic nerves. The central objective of this dissertation was to identify MRI parameters present at ADS that predict MS diagnosis. A scoring tool was first created containing 14 parameters identified from the literature and demonstrating substantial inter-rater agreement (Cohen’s kappa values ≥0.6). Children aged <16 years were enrolled at incident ADS and are currently followed for five years at 23 Canadian centers. Standardized MRI scans were acquired at onset and serially. MS was defined based on the occurrence of a second demyelinating attack or MRI evidence of new lesions in accordance with McDonald criteria for dissemination in time. Multivariable Cox proportional hazards regression models were used to identify MRI parameters that predicted MS diagnosis. Over 1100 MRI scans in 284 children with ADS were evaluated. To date, 57(20%) children have been diagnosed with MS. For those that developed MS, the median (IQR) time from incident attack to diagnosis was 6.2 (4.7-11.1) months. The presence of ≥1 T1-hypointense lesion (HR 20.6, 95% CI 5.5-78.0) and ≥1 T2 periventricular lesion (3.3, 1.3-8.8) were associated with an increased likelihood for MS diagnosis (sensitivity 84%, specificity 93%, PPV 76%, NPV 96%). The predictive parameters were validated in an independent Dutch cohort of 45 children with ADS (n=15, 33% MS): sensitivity 93%, specificity 87%, PPV 78%, NPV 96%. Finally, it was determined that the 2010 McDonald criteria are applicable for diagnosis of pediatric-onset MS diagnosis in older children with non-ADEM presentations. The work embodied herein emphasizes the value of MRI in predicting MS diagnosis in children with incident ADS. Early identification of children with MS is important for planning clinical care and will be valuable in future pediatric MS treatment trials.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".