An Observational Comparison Of Natalizumab & Fingolimod Using JCV Serology To Randomize Therapy (P7.205)
Bibliographic record
Abstract
*RC and DR contributed equally to authorship. OBJECTIVE: To directly compare the efficacy of natalizumab and fingolimod in an MS cohort utilizing a novel method of patient selection to minimize bias. BACKGROUND: Existing observational data comparing natalizumab and fingolimod is hindered by confounding. In some patients, JCV serology is used to determine treatment between natalizumab and fingolimod. Many studies have failed to show a convincing role of JCV exposure and infection in multilple sclerosis, thus it could mimic randomization and enable a direct comparison of treatment groups. DESIGN/METHODS: We reviewed prospectively collected, validated data for all relapsing-remitting MS patients started on either fingolimod or natalizumab where JCV serology was used to determine treatment assignment. We analyzed each group for time to first relapse and in a second analysis, time to first relapse or gadolinium enhancing lesion. RESULTS: 106 patients met our inclusion criteria and had adequate follow-up for analysis: 70 in the natalizumab group and 36 in the fingolimod group. The groups did not vary significantly in their baseline clinical characteristics at the time of treatment switch, suggesting that JCV serology does mimic randomization. Mean follow-up was 1.1 years for fingolimod and 1.2 years for natalizumab. There was a trend favoring natalizumab in time to first relapse, although this was not statistically significant (HR=2.26; 95% CI: 0.93, 5.51; p=0.072). There was, however, a significant difference in the secondary outcome, time to first relapse or gadolinium enhancing lesion event (HR=2.38; 95% CI: 1.09, 5.18; p=0.029) favoring natalizumab. CONCLUSIONS: The simultaneous availability of fingolimod and JCV serology enabled a novel observational comparison of fingolimod and natalizumab with minimal confounding by indication. With additional follow up of the cohort, our method may provide useful Class III data regarding comparative efficacy of natalizumab and fingolimod. Study Supported by: RC received a clinical fellowship training grant from the National Multiple Sclerosis Society. DR received a post-doctoral fellowship from the Multiple Sclerosis Society of Canada.
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".