Long-term follow-up of patients treated with glatiramer acetate: a multicentre, multinational extension of the European/Canadian double-blind, placebo-controlled, MRI-monitored trial
Bibliographic record
Abstract
Glatiramer acetate (GA) is effective in reducing clinical and magnetic resonance imaging (MRI) activity in relapsing-remitting multiple sclerosis (RRMS). Serial long-term MRI data are lacking for large cohorts of GA-treated patients. The European/Canadian GA study consisted of two consecutive phases, each lasting nine months. The first treatment phase was randomized, double-blind and placebo-controlled. The second was an open-label, active treatment phase with daily administration of 20 mg GA subcutaneously for all patients. For the long-term follow-up (LTFU), dual echo, pre- and postgadolinium T1-weighted brain MRI scans were obtained with the same acquisition scheme as for the original trial and a neurological assessment was performed. Lesion volumes, normalized brain volumes and percentage brain volume changes (PBVC) were measured. One hundred and forty-two (63.4%) of the 224 patients who completed the two phases of the European/Canadian study underwent the LTFU after a mean period of 5.8 years (range: 5.3-6.4); 73 were treated with GA from study initiation. MRI measures at LTFU did not significantly differ between patients originally assigned to placebo and those who were always treated with GA, but the proportion of patients who did not require walking aids at LTFU was lower in the latter group (P=0.034). PBVC between baseline and LTFU was significantly correlated with lesion load at study entry. An earlier initiation of GA treatment in patients with active RRMS might, at least partially, have a favourable impact on long-term disease evolution.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".