Neutralizing antibodies to interferon beta-1b multiple sclerosis: a clinico-radiographic paradox in the BEYOND trial
Bibliographic record
Abstract
BACKGROUND: The frequency and impact of neutralizing antibodies (NAbs) to interferon beta-1b (IFNβ-1b) on clinical and radiographic outcomes is controversial. OBJECTIVE: To assess NAb impact in the BEYOND study. METHODS: 2244 patients were randomized (2:2:1) to receive IFNβ-1b, either 250 or 500 µg, or glatiramer acetate, 20 mg, and observed for 2-3.5 years. NAb titers were determined every 6 months. A titer ≥20 NU/ml was considered NAb positive. Efficacy was compared between NAb-positive and NAb-negative patients, using comprehensive statistical analyses, taking into account the delayed appearance of NAbs, the time-dependent changes in the relapse rate, spontaneous reversions to NAb-negative status, NAb-titer level, and also adjusting for baseline factors. RESULTS: In the IFNβ-1b 250 µg group, NAb-positive titers were detected (≥ once) in 319 patients (37.0%); of these, 112 (35.1%) reverted to NAb-negative status. In the IFNβ-1b 500 µg group, 340 patients (40.7%) became NAb-positive and 119 (35.0%) reverted to NAb-negative status. In both IFNβ groups, especially the 250 µg arm, NAb-positive status was not associated with a convincing impact on any clinical outcome measure by any statistical analysis. By contrast, in both IFNβ groups, NAbs were associated with a very consistent deleterious impact on most MRI outcomes. CONCLUSION: There was a notable dissociation between the impact of NAbs on MRI and clinical outcomes. On MRI measures, the impact was consistent and convincing, whereas on clinical measures a negative impact of NAbs was not found. The basis for this clinico-radiographic paradox is unknown but it suggests that the relationship between NAbs and the therapeutic effects of IFNβ-1b is complex.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".