Bibliographic record
Abstract
Conventional MRI is a sensitive tool to identify focal white matter inflammation in patients with multiple sclerosis (MS). Despite a limited correlation between MRI lesions and relapses in untreated individuals,1 gadolinium-enhancing lesion activity correlates with treatment response,2 and predicts relapse outcomes in phase III clinical trials.3 For this reason, new lesions on MRI are the standard primary outcome of phase II clinical trials in relapsing-remitting MS (RRMS). The potential use of MRI biomarkers to compare different MS therapies to each other is also attractive. Comparison of the results of different pivotal trials, although dangerous, has suggested to some that high-dose, high-frequency administration of interferon-β (IFNβ) may have a faster onset of action than glatiramer acetate (GA). This has led to speculation that IFNβ may be more effective than GA, at least in the early stages of treatment. In this issue of Neurology ®, Cadavid et al.4 report the results of the BECOME trial, a head-to-head comparison which sought to address this question. In this industry-sponsored, investigator-conducted trial, 75 patients with RRMS were randomized to receive …
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".