Improved Clinical and MRI Disease Activity Outcomes, Including Slowing of Brain Volume Loss, in Alemtuzumab-Treated RRMS Patients: 8-Year Follow-up of CARE-MS I (TOPAZ Study)
Bibliographic record
Abstract
[Pelletier, Daniel] Univ Southern Calif, Keck Sch Med, Los Angeles, CA USA. [Arnold, Douglas L.] NeuroRx Res, Montreal, PQ, Canada. [Arnold, Douglas L.] McGill Univ, Montreal Neurol Inst, Montreal, PQ, Canada. [Boyko, Alexey N.] Pirogov Russian Natl Res Univ, Moscow, Russia. [Boyko, Alexey N.] Usupov Hosp, Demyelinating Dis Ctr, Moscow, Russia. [Comi, Giancarlo] Univ Vita Salute San Raffaele, Milan, Italy. [Hartung, Hans-Peter] Heinrich Heine Univ, Dusseldorf, Germany. [Havrdova, Eva Kubala] Charles Univ Prague, Fac Med 1, Prague, Czech Republic. [Inshasi, Jihad Said] Rashid Hosp, Dubai, U Arab Emirates. [Inshasi, Jihad Said] Dubai Med Coll, Dubai, U Arab Emirates. [McCombe, Pamela] Univ Queensland, Brisbane, Qld, Australia. [Nakamura, Kunio] Cleveland Clin, Cleveland, OH 44106 USA. [Oreja-Guevara, Celia] Univ Hosp San Carlos, Madrid, Spain. [Pozzilli, Carlo] Univ Rome, Rome, Italy. [Selmaj, Krzysztof W.] Univ Warmia & Mazury, Olsztyn, Poland. [Scott, Thomas F.] Allegheny Gen Hosp, Pittsburgh, PA 15212 USA. [Chung, Luke; Daizadeh, Nadia; Afsar, Salman] Sanofi, Cambridge, MA USA. [Van Wijmeersch, Bart] Hasselt Univ, BIOMED, Rehabil & MS Ctr Overpelt, Overpelt, Belgium.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".