MTR recovery in brain lesions in the BECOME study of glatiramer acetate vs interferon β-1b
Bibliographic record
Abstract
OBJECTIVE: To compare magnetization transfer changes in new brain MRI lesions identified during monthly imaging in patients with multiple sclerosis (MS) randomized to treatment with 250 μg subcutaneous interferon-β-1b (IFN-β-1b) every other day or daily 20 mg glatiramer acetate (GA) in a post hoc study using data from the Betaseron Versus Copaxone for Relapsing Remitting or CIS Forms of MS Using Triple Dose Gad 3 T MRI (BECOME) trial. METHODS: T1-weighted images acquired with and without fat saturation pulses in the BECOME study were evaluated and found to exhibit magnetization transfer ratio (MTR) effects, and were used to compute MTR images (FSMTR). Forty-three participants who had the required imaging and new lesions, from the 75 originally randomized into the BECOME study, were included in this post hoc analysis and evaluated longitudinally during treatment to determine FSMTRDrop, an experimental measure of the completeness of FSMTR recovery in new lesions. Two sets of new brain MRI lesions were defined, one based on the appearance of gadolinium contrast enhancement (Gd lesions) and the other based on FSMTR decreases (ΔFSMTR lesions). RESULTS: A total of 887 Gd lesions were identified in 43 participants (19 GA, 24 IFN-β-1b) and 321 ΔFSMTR lesions in 32 participants (16 GA, 16 IFN-β-1b). Participants randomized to GA exhibited greater average postlesion FSMTR recovery than did those randomized to IFN-β-1b in both Gd (p < 0.0001) and ΔFSMTR (p < 0.0001) lesions. CONCLUSIONS: New brain lesions that developed during treatment with GA exhibited evidence of greater FSMTR recovery than during treatment with IFN-β-1b. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that MTR recovery in patients with MS with new MRI brain lesions is greater with GA than with IFN-β-1b.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".