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 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 | 0.001 | 0.004 |
| 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 | 0.001 | 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".