Development of a Standardized MRI Scoring Tool for CNS Demyelination in Children
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The degree to which MR imaging is useful in the diagnosis of MS is predicated on standardized and reliable evaluation of MR imaging parameters. We aimed to devise items for an MR imaging scoring tool that would have high inter-rater agreement and would be straightforward to apply. MATERIALS AND METHODS: On the basis of a literature search and consensus of an expert panel, we identified 48 parameters that describe acute CNS demyelination, predict MS diagnosis, or characterize demyelinating disorder mimics. MR images of children with clinically confirmed MS, monophasic ADEM, and angiography-negative biopsy-positive small-vessel primary angiitis of the CNS were scored by 2 neuroradiologists independently, using the preliminary 48-parameter tool. Parameters with Cohen κ ≥ 0.6 and deemed important in predicting diagnosis were retained. Parameters not visualized on routine clinical imaging or not important in differentiating MS, ADEM, and SV-cPACNS were discarded. RESULTS: Of 65 eligible patients, 55 children were enrolled (16 with monophasic ADEM, 27 with MS, 12 with SV-cPACNS); 10 were excluded (6 had hard-copy films, 4 did not meet MR imaging quality requirements). Of the 48 parameters, 16 were retained in the final scoring tool. The remaining 28 parameters were discarded: 4 had κ < 0.6 and were not deemed useful in predicting diagnosis; 9 were not visible on routinely acquired clinical images; and 15 had inter-rater agreement ≥0.6 but were not useful in differentiating monophasic ADEM, MS, and SV-cPACNS. CONCLUSIONS: We propose a 16-parameter MR imaging scoring tool that is straightforward to apply in the clinical setting and demonstrates high inter-rater agreement.
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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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".