Durable reduction in MRI disease activity with alemtuzumab in treatment-naive patients with active relapsing-remitting multiple sclerosis: 6-year follow-up of the CARE-MS I study
Bibliographic record
Abstract
Sanofi Genzyme and Bayer HealthCare Pharmaceuticals. \n \nDLA: Compensation for serving as a speaker, consulting, and advisory board participant, and receiving research support (Acorda, Bayer, Biogen, Canadian Institutes of Health Research, Eli Lilly, EMD Serono, Genentech, GlaxoSmithKline, MedImmune, Merck Serono, MS Society of Canada, NeuroRx Research, Novartis, Opexa Therapeutics, Receptos, Roche, Sanofi, Sanofi Genzyme, and Teva). \n \nGC: Consulting fees (Actelion, Bayer, Merck Serono, Novartis, Sanofi Genzyme, and Teva); lecture fees (Bayer, Biogen Dompe, Merck Serono, Novartis, Sanofi Genzyme, Serono Symposia International Foundation, and Teva). \n \nGG: Consulting and/or grant/research support (Abbvie, Bayer, Biogen, Canbex Therapeutics, Five Prime Therapeutics, GlaxoSmithKline, GW Pharma, Merck, Merck Serono, Novartis, Oxford Pharmagenesis, Protein Discovery Laboratories, Roche, Sanofi Genzyme, Synthon, Teva Neuroscience, and UCB). \n \nDP: Consulting and/or speaking fees, and grant/research support (Biogen, Merck Serono, Novartis, Roche, Sanofi Genzyme, and Vertex). \n \nAR: Consulting and/or speaking fees (Bayer, Biogen, Bracco, Novartis, Sanofi Genzyme, and Stendhal). \n \nBVW: Research and travel grants, honoraria for MS expert advice and speaker's fees (Bayer-Schering, Biogen, Merck-Serono, Novartis, Roche, Sanofi Genzyme, and Teva). \n \nAT: Consulting and/or speaking fees, and grant/research support (Biogen, Chugai, Roche, Sanofi Genzyme, and Teva).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".