Clinical Predictors Of Expanded Disability Status Scale Rank Change Over 5-Year Intervals In The MSBase Registry (P4.187)
Bibliographic record
Abstract
OBJECTIVE: To determine clinical predictors of Expanded Disability Status Scale (EDSS) rank change over 5-year intervals as potential markers of multiple sclerosis (MS) severity, using an international cohort in the MSBase Registry. BACKGROUND: Given clinical heterogeneity in MS, it is desirable to define predictors of later outcomes. Using the MSBase Registry, we previously showed that EDSS ranking allows prediction of 5-year disability outcome. METHODS: Two MSBase Registry cohorts, in which EDSS rank change was previously described, were analysed. We included patients with relapsing remitting MS (RRMS) with 5-year prospectively acquired EDSS data. Intervals were years 1-6 (‘early RRMS’) and years 5-10 (‘later RRMS’) after CIS. As change in EDSS rank was statistically skewed, quantile (median) regression was performed to assess predictors of rank change over the 5-year intervals. Predictors included age, sex and imaging findings with stratification by EDSS, disease-modifying treatment (DMT) and annualised relapse rate (ARR). RESULTS: In the ‘early RRMS’ group, age and ARR ‘on treatment’ predicted EDSS rank worsening, whereas DMT possession ratio (over 50% during interval) predicted rank improvement. In the ‘later RRMS’ group, ARR ‘on treatment’ also predicted rank change but ARR ‘off treatment’ and DMT possession ratio (>50%) had no effect. Male sex and infratentorial MRI lesions were also predictive of worse outcome in the ‘later RRMS’ group. CONCLUSIONS: This is the first study evaluating predictors of EDSS rank change over 5-year intervals. The positive impact of treatment was observed in the early (but not the later) cohort in this study, supporting the case for early treatment in MS. The study confirms the relevance of on-treatment relapses for worse disability outcomes. Study Support: The MSBase Registry is supported by the independent MSBase Foundation Ltd which receives financial support from Merck Serono, Biogen Idec, Novartis, Bayer Schering and Sanofi Aventis.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".