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 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".