Early clinical markers of aggressive multiple sclerosis
Bibliographic record
Abstract
Abstract Patients with the ‘aggressive’ form of MS accrue disability at an accelerated rate, typically reaching EDSS >= 6 within 10 years of symptom onset. Several clinicodemographic factors have been associated with aggressive MS, but less research has focused on clinical markers that are present in the first year of disease. The development of early predictive models of aggressive MS is essential to optimise treatment in this MS subtype. We evaluated whether patients who will develop aggressive MS can be identified based on early clinical markers, and to replicate this analysis in an independent cohort. Patient data were obtained from MSBase. Inclusion criteria were (a) first recorded disability score (EDSS) within 12 months of symptom onset, (b) at least 2 recorded EDSS scores, and (c) at least 10 years of observation time. Patients were classified as having ‘aggressive MS’ if they: (a) reached EDSS >= 6 within 10 years of symptom onset, (b) EDSS >=6 was confirmed and sustained over >=6 months, and (c) EDSS >=6 was sustained until the end of follow-up. Clinical predictors included patient variables (sex, age at onset, baseline EDSS, disease duration at first visit) and recorded relapses in the first 12 months since disease onset (count, pyramidal signs, bowel-bladder symptoms, cerebellar signs, incomplete relapse recovery, steroid administration, hospitalisation). Predictors were evaluated using Bayesian Model Averaging (BMA). Independent validation was performed using data from the Swedish MS Registry. Of the 2,403 patients identified, 145 were classified as having aggressive MS (6%). BMA identified three statistical predictors: age > 35 at symptom onset, EDSS >= 3 in the first year, and the presence of pyramidal signs in the first year. This model significantly predicted aggressive MS (AUC = .80, 95% CIs = .75, .84). The presence of all three signs was strongly predictive, with 32% of such patients meeting aggressive disease criteria. The absence of all three signs was associated with a 1.4% risk. Of the 556 eligible patients in the Swedish MS Registry cohort, 34 (6%) met criteria for aggressive MS. The combination of all three signs was also predictive in this cohort (AUC = .75, 95% CIs = .66, .84). Taken together, these findings suggest that older age at symptom onset, greater disability during the first year, and pyramidal signs in the first year are early indicators of 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".