200 AFFINITY: opicinumab in a targeted population of MS patients
Bibliographic record
Abstract
Introduction Opicinumab is a human monoclonal antibody against LINGO-1, a negative regulator of oligodendrocyte differentiation and axonal regeneration. The phase 2 SYNERGY study identified a patient subpopulation with a potentially enhanced response to opicinumab treatment. Methods AFFINITY ( NCT03222973 ) is an ongoing, randomised, double-blind, placebo-controlled phase 2 study to evaluate the efficacy/safety of opicinumab vs placebo as an add-on to disease-modifying therapies in a targeted population. Inclusion criteria include: age 18–58; relapsing multiple sclerosis (MS) with ≤20-year duration; Expanded Disability Status Scale (EDSS) score 2.0–6.0; evidence of disease activity within 24 months prior to enrolment; brain MRI criteria suggestive of low myelin content but preserved tissue integrity in pre-existing T2 lesions; stably treated ≥24 weeks with interferon-beta, dimethyl fumarate, or natalizumab. Primary endpoint is the multicomponent Overall Response Score (based on EDSS, Timed 25-Foot Walk, and 9-Hole Peg Test). Secondary endpoints will evaluate confirmed improvement in disability measures. Results Enrolment began in September 2017 and was completed in September 2018. Participant baseline demographic, disease, and MRI characteristics will be presented. Conclusions AFFINITY will further investigate the efficacy and safety of opicinumab in a subpopulation of MS patients. Support: Biogen. Disclosures to be included on poster.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".