Use of natalizumab in persons with multiple sclerosis: 2022 update
Bibliographic record
Abstract
BACKGROUND: Natalizumab is a humanized monoclonal antibody used for treatment of highly active relapsing-remitting multiple sclerosis (MS). With more than 15 years of post-marketing experience with natalizumab in Canada, several real-world studies have shown the long-term efficacy and safety of natalizumab. In addition, risk stratification/mitigation strategies for progressive leukoencephalopathy (PML), an adverse effect associated with natalizumab based on the John Cunningham virus (JCV) index; treatment duration beyond 24 months; and prior exposure to immunosuppressant drugs have been developed. METHODS: A group of neurologists from various MS clinics across Canada met in September 2021 to update the 2015 Canadian practice recommendations for the use of natalizumab in persons with MS (PwMS). RESULTS: The recommendations focused on the long-term efficacy and safety data from real-world studies, patient selection according to JCV index criteria, risk management strategies for PML (including extended interval dosing), and options for switching to currently available disease-modifying therapies for MS. CONCLUSIONS: The recommendations of clinical neurologists seek to optimize the management of PwMS who may benefit from treatment with natalizumab.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.005 | 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".