Predictors of long‐term disability accrual in relapse‐onset multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: To identify predictors of 10-year Expanded Disability Status Scale (EDSS) change after treatment initiation in patients with relapse-onset multiple sclerosis. METHODS: Using data obtained from MSBase, we defined baseline as the date of first injectable therapy initiation. Patients need only have remained on injectable therapy for 1 day and were monitored on any approved disease-modifying therapy, or no therapy thereafter. Median EDSS score changes over a 10-year period were determined. Predictors of EDSS change were then assessed using median quantile regression analysis. Sensitivity analyses were further performed. RESULTS: We identified 2,466 patients followed up for at least 10 years reporting post-baseline disability scores. Patients were treated an average 83% of their follow-up time. EDSS scores increased by a median 1 point (interquartile range = 0-2) at 10 years post-baseline. Annualized relapse rate was highly predictive of increases in median EDSS over 10 years (coeff = 1.14, p = 1.9 × 10(-22) ). On-therapy relapses carried greater burden than off-therapy relapses. Cumulative treatment exposure was independently associated with lower EDSS at 10 years (coeff = -0.86, p = 1.3 × 10(-9) ). Furthermore, pregnancies were also independently associated with lower EDSS scores over the 10-year observation period (coeff = -0.36, p = 0.009). INTERPRETATION: We provide evidence of long-term treatment benefit in a large registry cohort, and provide evidence of long-term protective effects of pregnancy against disability accrual. We demonstrate that high annualized relapse rate, particularly on-treatment relapse, is an indicator of poor prognosis. Ann Neurol 2016;80:89-100.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".