Prediction of relapse activity when switching to cladribine for multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Patients with relapsing-remitting multiple sclerosis commonly switch between disease-modifying therapies (DMTs). Identifying predictors of relapse when switching could improve outcomes. OBJECTIVE: To determine predictors of relapse hazard when switching to cladribine. METHODS: Data of patients who switched to cladribine, grouped by prior disease-modifying therapy (pDMT; interferon-β/glatiramer acetate, dimethyl fumarate, teriflunomide, fingolimod or natalizumab (NTZ)), were extracted from the MSBase Registry. Predictors of relapse hazard during the treatment gap and the first year of cladribine therapy were determined. RESULTS: Of 513 patients, 22 relapsed during the treatment gap, and 38 within 1 year of starting cladribine. Relapse in the year before pDMT cessation predicted treatment gap relapse hazard (hazard ratio (HR) = 2.43, 95% confidence interval (CI) = 1.03-5.71). After multivariable adjustment, relapse hazard on cladribine was predicted by relapse before pDMT cessation (HR = 2.00, 95% CI = 1.01-4.02), treatment gap relapse (HR = 6.18, 95% confidence interval (CI) = 2.65-14.41), switch from NTZ (HR compared to injectable therapies 4.08, 95% CI = 1.35-12.33) and age at cladribine start (HR = 0.96, 95% CI = 0.91-0.99). CONCLUSION: Relapse during or prior to the treatment gap, and younger age, are of prognostic relevance in the year after switching to cladribine. Switching from NTZ is also independently associated with greater relapse hazard.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 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".