Heterogeneity on long-term disability trajectories in patients with secondary progressive MS: a latent class analysis from Big MS Data network
Bibliographic record
Abstract
BACKGROUND: Over the decades, several natural history studies on patients with primary (PPMS) or secondary progressive multiple sclerosis (SPMS) were reported from international registries. In PPMS, a consistent heterogeneity on long-term disability trajectories was demonstrated. The aim of this study was to identify subgroups of patients with SPMS with similar longitudinal trajectories of disability over time. METHODS: All patients with MS collected within Big MS registries who received an SPMS diagnosis from physicians (cohort 1) or satisfied the Lorscheider criteria (cohort 2) were considered. Longitudinal Expanded Disability Status Scale (EDSS) scores were modelled by a latent class growth analysis (LCGA), using a non-linear function of time from the first EDSS visit in the range 3-4. RESULTS: A total of 3613 patients with SPMS were included in the cohort 1. LCGA detected three different subgroups of patients with a mild (n=1297; 35.9%), a moderate (n=1936; 53.6%) and a severe (n=380; 10.5%) disability trajectory. Median time to EDSS 6 was 12.1, 5.0 and 1.7 years, for the three groups, respectively; the probability to reach EDSS 6 at 8 years was 14.4%, 78.4% and 98.3%, respectively. Similar results were found among 7613 patients satisfying the Lorscheider criteria. CONCLUSIONS: Contrary to previous interpretations, patients with SPMS progress at greatly different rates. Our identification of distinct trajectories can guide better patient selection in future phase 3 SPMS clinical trials. Additionally, distinct trajectories could reflect heterogeneous pathological mechanisms of progression.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".