Prediction of multiple sclerosis outcomes when switching to ocrelizumab
Bibliographic record
Abstract
Background: Increasingly, people with relapsing-remitting multiple sclerosis (RRMS) are switched to highly effective disease-modifying therapies (DMTs) such as ocrelizumab. Objective: To determine predictors of relapse and disability progression when switching from another DMT to ocrelizumab. Methods: Patients with RRMS who switched to ocrelizumab were identified from the MSBase Registry and grouped by prior disease-modifying therapy (pDMT; interferon-β/glatiramer acetate, dimethyl fumarate, teriflunomide, fingolimod or natalizumab) and washout duration (<1 month, 1–2 months or 2–6 months). Survival analyses including multivariable Cox proportional hazard regression models were used to identify predictors of on-ocrelizumab relapse within 1 year, and 6-month confirmed disability progression (CDP). Results: After adjustment, relapse hazard when switching from fingolimod was greater than other pDMTs, but only in the first 3 months of ocrelizumab therapy (hazard ratio (HR) = 3.98, 95% confidence interval (CI) = 1.57–11.11, p = 0.004). The adjusted hazard for CDP was significantly higher with longer washout (2–6 m compared to <1 m: HR = 9.57, 95% CI = 1.92–47.64, p = 0.006). Conclusion: The risk of disability worsening during switch to ocrelizumab is reduced by short treatment gaps. Patients who cease fingolimod are at heightened relapse risk in the first 3 months on ocrelizumab. Prospective evaluation of strategies such as washout reduction may help optimise this switch.
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How this classification was reachedexpand
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".