Proportion of alemtuzumab-treated patients converting from relapsing-remitting multiple sclerosis to secondary progressive multiple sclerosis over 6 years
Bibliographic record
Abstract
BACKGROUND: Few data exist concerning conversion to secondary progressive MS in patients treated with disease-modifying therapies. OBJECTIVE: Determine the proportion of alemtuzumab-treated patients converting from relapsing-remitting to secondary progressive MS during the CARE-MS core and extension studies. METHODS: = 811) were analyzed post hoc for secondary progressive MS conversion. Optimal conversion definition: Expanded Disability Status Scale (EDSS) score ≥4, pyramidal functional system score ≥2, and confirmed progression over ≥3 months including confirmation within the functional system leading to progression, independent of relapse. RESULTS: Over 6.2 years median follow-up, 20 alemtuzumab-treated patients converted (Kaplan-Meier estimate, 2.7%; 95% confidence interval, 1.8%-4.2%). Sensitivity analysis accounting for dropouts showed similar results (3%), as did analyses using alternative definitions with different EDSS thresholds and/or confirmation periods, and analysis of core study subcutaneous interferon beta-1a-treated patients who received alemtuzumab in the extension. Patients converting to secondary progressive MS were older, and had higher EDSS scores and greater brain lesion volumes at baseline, but did not need additional alemtuzumab or other therapies. CONCLUSIONS: NCT00530348, NCT00548405, NCT00930553.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".