Advancing the Understanding of Progression in Multiple Sclerosis: An Interview With Shibeshih Belachew
Bibliographic record
Abstract
Shibeshih Belachew speaks to Laura Dormer, Commissioning Editor: Dr Shibeshih Belachew, MD, PhD, is a Senior Medical Director for Multiple Sclerosis (MS) Disease Area in Global Product Development Medical Affairs at Roche (Basel, Switzerland). Prior to joining Roche in January 2016, he was Director of MS Franchise and Head of Medical Director's office for Biogen Region Europe and Canada. Previously at Biogen he also served as a Director in Global Neurology for the natalizumab program in Cambridge (MA, USA). Prior to joining industry, he was a Clinical Professor of Neurology at the University of Liège in Belgium. Shibeshih completed neurology postgraduate training at the University of Liège and has a PhD in Biomedical Science in the field of Developmental Neurobiology. Shibeshih has been a post-doctoral fellow in the Laboratory of Cellular and Synaptic Neurophysiology at the National Institutes of Health (Bethesda, MD, USA) and later at the Center for Neuroscience Research of Children's National Medical Center in Washington DC. He is a member of the Belgian Neurological Society.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.032 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".