Acute central nervous system inflammation following COVID-19 vaccination: An observational cohort study
Bibliographic record
Abstract
Background: Reports suggest a potential association between coronavirus disease 2019 (COVID-19) vaccines and acute central nervous system (CNS) inflammation. Objective: The main objective of this study is to describe features of acute CNS inflammation following COVID-19 vaccination. Methods: A retrospective observational cohort study was performed at the BARLO MS Centre in Toronto, Canada. Clinicians reported acute CNS inflammatory events within 60 days after a COVID-19 vaccine from March 2021 to August 2022. Clinical characteristics were evaluated. Results: Thirty-eight patients (median age 39 (range: 20–82) years; 60.5% female) presented within 0–55 (median 15) days of a receiving a COVID-19 vaccine and were diagnosed with relapsing remitting multiple sclerosis (MS) ( n = 16), post-vaccine transverse myelitis ( n = 7), clinically isolated syndrome ( n = 5), MS relapse ( n = 4), tumefactive demyelination ( n = 2), myelin oligodendrocyte glycoprotein antibody disease ( n = 1), neuromyelitis optica spectrum disorder ( n = 1), chronic lymphocytic inflammation with pontine perivascular enhancement responsive to steroids ( n = 1) and primary autoimmune cerebellar ataxia ( n = 1). Twenty-two received acute treatment and 21 started disease-modifying therapy. Sixteen received subsequent COVID-19 vaccination, of which 87.5% had no new or worsening neurological symptoms. Conclusion: To our knowledge, this is the largest study describing acute CNS inflammation after COVID-19 vaccination. We could not determine whether the number of inflammatory events was higher than expected.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".