Reduced effectiveness of long-term interferon-β treatment on relapses in neutralizing antibody-positive multiple sclerosis patients: a Canadian multiple sclerosis clinic-based study
Bibliographic record
Abstract
Multiple sclerosis (MS) patients treated with interferon-beta (IFN-beta) often form anti-IFN-beta antibodies accompanied by a reduction in IFN-beta bioavailability. The clinical effect of these antibodies remains controversial. MS patients in British Columbia, Canada, must be diagnosed and evaluated annually by neurologists in an MS clinic in order to be reimbursed for their IFN-beta prescriptions. We have identified at the UBC MS clinic a cohort of 262 patients, each having been treated with a single IFN-beta preparation more than three years, some for nearly a decade. Of 119 patients treated with Betaseron (IFN-beta1b), 18 (15.1%) were neutralizing antibody positive (NAb+) at the time of the study, whereas of 131 treated with subcutaneous Rebif (IFN-beta1a SC), 16 (12.2%) were NAb+, but none of 12 treated with intramuscular Avonex (IFN-beta1a) had detectable neutralizing antibodies. During the first two years of treatment, the relapse rate was significantly reduced from pre-treatment rates (P<0.001) and appeared to be unaffected by the subsequent NAb status. However, the relapse rates in the NAb+ patients were significantly greater than in the NAb- patients during years 3 (P<0.010) and 4 (P<0.027). Betaseron-treated NAb+ patients tended to have more relapses than NAb- patients during year 3 and this almost reached significance (P=0.056) but their relapse rate did not differ in year 4 and later. In contrast, Rebif-treated NAb+ patients tended to have more relapses in year 3 than Rebif-treated NAb- patients (P=0.074), but in year 4 they clearly (P=0.009) had more relapses than Rebif-treated NAb- patients. There was no convincing effect on progression of disability in any group.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".