Glatiramer acetate in combination with minocycline in patients with relapsing—remitting multiple sclerosis: results of a Canadian, multicenter, double-blind, placebo-controlled trial
Bibliographic record
Abstract
Minocycline is proposed as an add-on therapy to improve the efficacy of glatiramer acetate in relapsing-remitting multiple sclerosis. The effect of minocycline plus glatiramer acetate was evaluated in this double-blind, placebo-controlled study by determining the total number of T1 gadolinium-enhanced lesions at months 8 and 9 in patients who were starting glatiramer acetate and had at least one T1 gadolinium-enhanced lesion on screening magnetic resonance imaging. Forty-four participants were randomized to either minocycline 100 mg twice daily or matching placebo for 9 months as add-on therapy. They were assessed at screening and months 1, 3, 6, 8 and 9. Forty participants completed the study. Compared with glatiramer acetate/placebo, glatiramer acetate/minocycline reduced the total number of T1 gadolinium-enhanced lesions by 63% (mean 1.47 versus 2.95; p = 0.08), the total number of new and enlarging T2 lesions by 65% (mean 1.84 versus 5.14; p = 0.06), and the total T2 disease burden (p = 0.10). A higher number of gadolinium-enhanced lesions were present in the glatiramer acetate/minocycline group at baseline; this was incorporated into the analysis of the primary endpoint but makes interpretation of the data more challenging. The risk of relapse tended to be lower in the combination group (0.19 versus 0.41; p = NS). Treatment was safe and well tolerated. We conclude that efficacy endpoints showed a consistent trend favoring combination treatment. As minocycline is a relatively safe oral therapy, further study of this combination is warranted in relapsing-remitting multiple sclerosis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| 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) | 0.002 | 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".