ACCLAIM: A randomized trial of abatacept (CTLA4-Ig) for relapsing-remitting multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Costimulatory blockade of T lymphocytes with the CTLA4-Ig fusion protein abatacept could be an effective treatment for the immune-mediated neuroinflammatory disease relapsing-remitting multiple sclerosis (RRMS). OBJECTIVE: To evaluate efficacy and safety of abatacept in RRMS. METHODS: ACCLAIM (A Cooperative Clinical Study of Abatacept in Multiple Sclerosis) was a Phase II, randomized, double-blind, placebo-controlled, multi-center trial. In all, 65 of 123 planned participants with RRMS were randomized to monthly intravenous infusions of abatacept or placebo for 24 weeks in a 2:1 ratio, switched to the opposite treatment at 28 weeks, and received their final dose of study medication at 52 weeks. Enrollment was closed early due to slow accrual. The primary endpoint was the mean number of new gadolinium-enhancing (Gd+) lesions obtained on magnetic resonance imaging (MRI) scans performed every 4 weeks. RESULTS: No statistically significant differences were observed in mean number of new Gd+ MRI lesions between the abatacept and placebo groups. No statistically significant differences were observed in other MRI and clinical parameters of RRMS disease activity. Abatacept was well tolerated. CONCLUSION: The ACCLAIM study did not demonstrate efficacy of abatacept in reducing the number of new Gd+ MRI lesions, or clinical measures of disease activity in RRMS.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.007 | 0.001 |
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".