Cladribine versus fingolimod, natalizumab and interferon β for multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: This propensity score-matched analysis from MSBase compared the effectiveness of cladribine with interferon β, fingolimod or natalizumab. METHODS: We identified all patients with relapse-onset multiple sclerosis, exposure to the study therapies and ⩾1-year on-treatment follow-up from MSBase. Three pairwise propensity score-matched analyses compared treatment outcomes over 1 year. The outcomes were hazards of first relapse, disability accumulation and disability improvement events. Sensitivity analyses were completed. RESULTS: The cohorts consisted of 37 (cladribine), 1940 (interferon), 1892 (fingolimod) and 1410 patients (natalizumab). The probability of experiencing a relapse on cladribine was lower than on interferon ( p = 0.05), similar to fingolimod ( p = 0.31) and higher than on natalizumab ( p = 0.042). The probability of disability accumulation on cladribine was similar to interferon ( p = 0.37) and fingolimod ( p = 0.089) but greater than natalizumab ( p = 0.021). The probability of disability improvement was higher on cladribine than interferon ( p = 0.00017), fingolimod ( p = 0.0025) or natalizumab ( p = 0.00099). Sensitivity analyses largely confirmed the above results. CONCLUSION: Cladribine is an effective therapy for relapse-onset multiple sclerosis. Its effect on relapses is comparable to fingolimod and its effect on disability accrual is comparable to interferon β and fingolimod. Cladribine may potentially associate with superior recovery from disability relative to interferon, fingolimod and natalizumab.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".