Predictors of long‐term disability accrual in relapse‐onset multiple sclerosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it