Longitudinal analyses of the effects of neutralizing antibodies on interferon beta-1b in relapsing-remitting multiple sclerosis
Bibliographic record
Abstract
We have analysed data on exacerbation rates, Expanded Disability Status Scale (EDSS) scores, and lesion burdens using the results of two neutralizing antibody (NAB) assays (CPE and MxA) from the pivotal relapsing-remitting multiple sclerosis (MS) trial of interferon beta-1b (IFNB) with a longitudinal approach, where the influence of NABs in individual patients is assessed by comparing responses during NAB-positive and NAB-negative periods. There are apparent influences on exacerbation rate related to dose of IFNB, titer level, and duration of positivity. With the MxA assay, exacerbation rates after switching to NAB-positive status are estimated to be 28% higher [95% confidence interval (CI): (-15%, 92%)] and -2% higher [95% CI: (-21%, 21%)] on the low- and high-dose IFNB arms, respectively. When compared with all NAB-negative periods, exacerbation rates during NAB-positive periods are estimated to be 29% higher [95% CI: (0%, 67%)] and 18% higher [95% CI: (0%, 40%)] on the low- and high-dose IFNB arms, respectively. When NAB-positive patients again become NAB-negative, no evidence of increased exacerbation rates could then be demonstrated. More detailed exploratory analyses indicate that the effects are most evident in the approximately 20% of patients developing high titers. In these patients, the influence of NABs may be self-limited, as titers often diminish or NABs become undetectable with time.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".