The effectiveness of natalizumab vs fingolimod–A comparison of international registry studies
Bibliographic record
Abstract
BACKGROUND: Natalizumab and fingolimod were the first preparations recommended for disease breakthrough in priorly treated relapsing-remitting multiple sclerosis. Of three published head-to-head studies two showed that natalizumab is the more effective to prevent relapses and EDSS worsening. METHODS: By re-analyzing original published results from MSBase, France, and Denmark using uniform methodologies, we aimed at identifying the effects of differences in methodology, in the MS-populations, and at re-evaluating the differences in effectiveness between the two drugs. We gained access to copies of the individual amended databases and pooled all data. We used uniform inclusion/exclusion criteria and statistical methods with Inverse Probability Treatment Weighting. RESULTS: The pooled analyses comprised 968 natalizumab- and 1479 fingolimod treated patients. The on-treatment natalizumab/fingolimod relapse rate ratio was 0.77 (p=0.004). The hazard ratio (HR) for a first relapse was 0.82 (p=0.030), and the HR for sustained EDSS improvement was 1.4 (p=0.009). There were modest differences between each of the original published studies and the replication study, but the conclusions of the three original studies remained unchanged: in two of them natalizumab was more effective, but in the third there was no difference between natalizumab and fingolimod. CONCLUSION: The results were largely invariant to the epidemiological and statistical methods but differed between the MS populations. Generally, the advantage of natalizumab was confirmed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".