Predicting Response To Interferon beta-1b Therapy In Patients With Clinically Isolated Syndrome (P1.228)
Bibliographic record
Abstract
OBJECTIVE: To apply and validate the modRio score to discriminate response to interferon beta-1b (IFNB-1b) in patients with clinically isolated syndrome. BACKGROUND: Simple criteria for predicting treatment response in patients with first signs of multiple sclerosis (MS) are of great value. The modified Rio (modRio) score based on T2 lesions and clinical relapses over the first year of interferon therapy has shown prognostic value for predicting disease activity in the subsequent years. DESIGN/METHODS: This post hoc analysis included 260 patients (IFNB-1b eod) from the BENEFIT study with MRI assessment at Month 12 and >=2 scheduled visits after Year 1 during a 5-year follow up. The score (0-3) was based on the number of new T2 lesions (>5) and clinical relapses (0, 1, or 2) during the first year of therapy. RESULTS: Of the 260 subjects meeting the inclusion criteria, 202 (77.7%) were in the low risk group (modRio score=0), whereas 43 (16.5%) and 15 (5.8%) were intermediate and high risk (modRio score 1 and 2, respectively) with no patients reaching a score 3. Annualized relapse rate [95% CI] from Year 1 to 5 for modRio 0, 1, and 2 were 0.14 [0.12-0.17], 0.30 [0.22-0.39], and 0.62 [0.43-0.86]. Sensitivity, specificity, and accuracy of modRio score >=1 (vs. 0) to predict >=1 relapses/year post Year 1 were 60.0%, 80.0%, and 78.8%. Confirmed EDSS progression from Year 1 up to Year 5 was observed in 40/202 patients (Kaplan Meier Estimate [KME]: 21%) for modRio score 0, in 11/ 43 (KME: 26%) for 1 and 5/15 (KME: 35.4%) for modRio score 2. CONCLUSIONS: The modRio score predicted response to IFNB-1b. Most patients in the BENEFIT cohort were low risk (score=0) according to the modRio criteria, and had a lower frequency of disease progression compared with those with modRio criteria 1 and 2. Study Supported by: Bayer HealthCare Pharmaceuticals
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".