Baseline Predictors of Disease Activity in Patients with CIS Treated with Interferon beta-1b in the BENEFIT 11 Trial (P2.121)
Bibliographic record
Abstract
Objective: To analyze baseline patient characteristics for prediction of disease outcomes 11 years postrandomization. Background: A number of variables have been identified that are assessable early in the course of MS that may predict the disease course. Long-term follow up from the BENEFIT trial, which enrolled patients with clinically-isolated syndrome, is a unique opportunity to assess the predictive capacity of these variables. Methods: Patients were randomized to initial treatment with interferon beta-1b or placebo for 2 years or until diagnosis of clinically-definite multiple sclerosis (CDMS), after which they could take interferon beta-1b. Regression analyses including baseline age; sex; mono/multifocal onset; steroid treatment, EDSS score; gadolinium-enhancing (Gd+) and T2 lesion numbers; T2 and T1 hypointense lesion volume; presence of lesions in the spinal cord, optic nerve, or brainstem; PASAT score, and treatment assignment as covariates were conducted. P<.05 was used to identify significant associations. Results: 278 of 468 originally-randomized patients in BENEFIT were assessed at Year 11 (167, early treatment; 111, delayed treatment). In regression models, early treatment with interferon beta-1b, older age, lower number of Gd+ lesions, smaller T1 lesion volume, absence of steroid treatment, and absence of spinal cord lesions were significantly associated with longer time to CDMS. Older age, and male sex were associated with lower annualized relapse rate. Younger age, male sex, higher baseline EDSS, and lower Gd+ lesion count significantly predicted lower probability of a sustained 1-point EDSS progression. Higher likelihood of presence of clinical disease (≥1 relapse, CDMS, or EDSS progression) at Year 11 was significantly associated with higher number of Gd+ lesions. Conclusion: Analyses of patients after 11 years identified a number of baseline demographic, MRI, and clinical variables that predicted longer-term disease outcomes. Among other factors, assignment to earlier treatment with interferon beta-1b was associated with longer time to CDMS.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".