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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".