The long-term outcomes of CIS patients in the Barcelona inception cohort: Looking back to recognize aggressive MS
Bibliographic record
Abstract
OBJECTIVE: To explore the long-term outcomes of patients with clinically isolated syndromes from the Barcelona cohort. METHODS: We selected patients with a follow-up longer than 10 years to (1) estimate the risks of multiple sclerosis (MS) and disability accumulation according to the baseline number of T2 lesions and to compare treated versus untreated patients and early versus delayed treatment, and (2) to study baseline features of patients with aggressive MS (Expanded Disability Status Scale (EDSS) ⩾6.0 at 10 years). RESULTS: In all, 401 patients were included (mean follow-up of 14.4 (standard deviation of 2.9) years). A higher number of T2 lesions was associated with an earlier MS diagnosis and an earlier risk of irreversible disability. Early treatment was associated with a decreased risk of EDSS of 3.0: adjusted hazard ratio = 0.4, 95% confidence interval = (0.2, 0.7). Patients with aggressive MS differed in their baseline brain magnetic resonance images: The median (interquartile range) number of T2 lesions and contrast-enhancing lesions (CEL) was 71 (28-95) versus 7 (1-19) and 3 (1-24) versus 0 (0-1), respectively. The cut-offs that better classified patients with aggressive MS were 20 for T2 lesions and 2 for CEL. CONCLUSION: Although MS natural history is changing, a high lesion load at onset is helpful to identify patients at risk of presenting an aggressive 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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".