021 Determinants of natalizumab-associated PML outcomes
Bibliographic record
Abstract
Introduction Natalizumab treatment is associated with risk of progressive multifocal leukoencephalopa- thy (PML). Following PML diagnosis, plasma exchange (PLEX) may be used to enable rapid natalizumab clearance. This analysis explores the impact of PLEX and patient characteristics on natalizumab-associated PML outcomes. Methods Patients with multiple sclerosis, natalizumab-associated PML, and PLEX treatment status as of September 2018 were included (PLEX+, n=616; PLEX−, n=109). The primary outcome was 2-year survival after PML diagnosis. Kaplan-Meier estimates of cumulative survival for patients with/without PLEX were stratified by log JC virus (JCV) viral copy number (VCN) at PML diagnosis. Hazard ratios for survival were based on a Cox proportional hazards model. Results The cumulative probability of 2-year survival for PLEX+ vs PLEX− patients was 88.2% vs. 89.3% (P=0.857) with log VCN ≤5, 73.8% vs. 89.3% (P=0.097) with log VCN >5 to ≤7, and 68.2% vs. 78.9% (P=0.435) with log VCN >7. Improved survival was associated with younger age, asymptomatic presentation, localized PML lesions, and lower log JCV VCN. Conclusions PLEX had no significant effect on survival rates. Numerically worse 2-year survival probabilities were observed with PLEX regardless of PML presentation, suggesting PLEX is not effective for improving post-PML outcomes. Support Biogen. Disclosures: Included on the poster. g.giovannoni@qmul.ac.uk
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".