Autoimmunity and long-term safety and efficacy of alemtuzumab for multiple sclerosis: Benefit/risk following review of trial and post-marketing data
Bibliographic record
Abstract
Does preexisting or treatment-emergent autoimmunity increase the risk of subsequent autoimmune disease in individuals with relapsing-remitting multiple sclerosis (MS) after alemtuzumab? In the extended phase 2/3 trials, 34/96 (35.4%) patients with and 395/1120 (35.3%) without preexisting autoimmunity developed non-MS autoimmunity. Thyroid autoimmunity after alemtuzumab courses 1 or 2 did not increase subsequent non-thyroid autoimmune adverse events. Therefore, autoimmune disease before or after alemtuzumab treatment does not predict autoimmunity after further courses, so should not preclude adequate alemtuzumab dosing to control MS. Finally, post-marketing safety data contribute toward a full record of the alemtuzumab benefit/risk profile for the MS field.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".