Predictors of response to opicinumab in acute optic neuritis
Bibliographic record
Abstract
Abstract Objective The objective of this study was to evaluate prespecified and post hoc analyses inRENEWsubgroups to identify participants more likely to benefit from opicinumab. Methods RENEWassessed the efficacy/safety of opicinumab versus placebo in participants with a first unilateral acute optic neuritis (AON) episode. Difference in visual evoked potential (VEP) latency of the affected eye at 24 weeks versus the fellow eye at baseline was the primary endpoint. Interactions between the primary endpoint and prespecified baseline variables (including age, timing of treatment initiation, and visual impairment) using the median as cut‐off were evaluated in the per protocol population using analysis of covariance (ANCOVA); subgroups based on preexisting brain T2 lesion volume were also analyzed. Interactions between the primary endpoint and retinal ganglion cell layer/inner plexiform layer (RGCL/IPL) and retinal nerve fiber layer (RNFL) thickness were assessed post hoc as was weight gain by treatment. Results Treatment benefit of opicinumab (n = 33) over placebo (n = 36) on the primary endpoint was greatest in participants older than the median age at baseline (≥33 years); the difference versus placebo for baseline age ≥33 years was −14.17 msec [P = 0.01] versus −0.89 msec for baseline age <33 years, [P = 0.87]). Post hoc analysis showed thatVEPlatency recovery was significantly associated with lessRGCL/IPLthinning (P = 0.0164), occurring early on. Interpretation Age was the strongest prespecified baseline characteristic associated with a treatment effect of opicinumab. A strong association betweenVEPlatency recovery at week 24 and earlyRGCL/IPLpreservation was observed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".