060 Association of brain volume loss and neda outcomes in patients with relapsing multiple sclerosis in the opera i and opera ii studies (ENCORE)
Bibliographic record
Abstract
Introduction Brain volume loss (BVL) occurs in multiple sclerosis (MS) patients, reflecting irreversible tissue damage. No evidence of disease activity (NEDA), a composite measure assessing absence of clinical and magnetic resonance imaging (MRI) disease activity has emerged as important treatment goal in MS and may be associated with preservation of brain tissue and BVL prevention. Methods Patients in OPERA-I/II ( NCT01247324 /NCT01412333) received 600 mg ocrelizumab (intravenous) very 24 weeks or 44 µg subcutaneous interferon beta-1a (IFNβ-1a) 3x-weekly for 96 weeks. Brain MRI assessments were completed at baseline and 24/48/96 Weeks. Brain volume normalised for head size was measured using SIENAX software. Percent change in whole brain volume (WBV) was determined using SIENA software, changes in cortical grey (GMV) and white (WMV) matter volumes were measured using validated, locally developed Jacobian integrator atrophy software. NEDA was defined as absence of relapses, 12-week confirmed disability progression, T1 Gd–enhancing lesions and new and/or enlarging T2-lesions. Changes from baseline in brain volume were examined in NEDA patients and those with evidence of disease activity (EDA), using the mixed-effects model for repeated measures method. Results The analysis included 1520 patients (ocrelizumab-761; IFNβ-1a-759). Over 96 weeks, 569 (37%) patients [ocrelizumab-363 (48%); IFNβ-1a-206(27%); p<0.001] had NEDA. Compared with EDA patients, NEDA patients had significantly less WBV loss from baseline (30%-reduction; p<0.001). In the NEDA group, ocrelizumab patients had significantly less WBV loss (32%-reduction; p<0.001), WMV loss (34%-reduction; p=0.044) and GMV loss (30%-reduction; p<0.001) from baseline than IFNβ-1a patients. In the EDA group, ocrelizumab patients had significantly less WBV loss (11%-reduction; p=0.047) and GMV loss (21%-reduction; p<0.001) but not WMV loss (1.03%-increase; p=0.90) from baseline than IFNβ-1a patients. Conclusion These findings highlight the importance of NEDA as treatment goal with respect to brain tissue preservation regardless of treatment choice. Ocrelizumab may confer additional benefits in NEDA patients NEDA beyond what is observed with IFNβ-1a.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".