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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".