ACTRIMS-ECTRIMS MSBoston 2014: Late Breaking News Oral Presentations
Bibliographic record
Abstract
Background: RPC1063 is an oral, selective sphingosine 1-phosphate 1 receptor modulator in clinical development for the treatment of relapsing multiple sclerosis (RMS). Objectives: Demonstrate the superior efficacy of low (LD, 0.5 mg) and high (HD, 1.0 mg) dose RPC1063 vs. placebo (PBO), and characterize the safety of RPC1063 in patients with RMS. Methods: RADIANCE is an international, combined Phase 2/3 trial. In the 24-week, Phase 2 portion, 258 patients were randomized (1:1:1) to PBO (n=88), LD (n=87) or HD (n=83). The primary endpoint was the cumulative number of total gadoliniumenhancing (GdE) lesions on MRI at Wks 12, 16, 20 and 24. Key secondary endpoints included number of GdE lesions at Wk 24, cumulative number of new/enlarging T2 lesions from Wks 12-24, and annualized relapse rate (ARR). Safety was assessed using vital signs, labs, ECG, Holter monitoring, PFT, OCT and adverse events (AEs). Patients were titrated to their assigned dose over one week to mitigate first dose heart rate effects. Results: 98% of patients completed the trial. Cumulative total Wk 12-24 GdE lesions was reduced 86% in both RPC1063 arms vs. PBO (mean SD: PBO 11.1 29.9, LD 1.5 3.7, HD 1.5 3.4, both p< 0.0001). Numbers of GdE lesions at Wk 24 were significantly reduced 91 and 94% (PBO 3.2 9.8, LD 0.3 0.9, HD 0.2 0.6, both p< 0.0001) and cumulative Wks 12-24 new/enlarging T2 lesions 84 and 91% (PBO 9.0 20.9, LD 1.4 3.2, HD 0.8 1.9, both p< 0.0001). A favorable trend in ARR reduction was observed vs. PBO (LD: 31%, p=0.27; HD: 53%, p=0.053).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.222 | 0.126 |
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".