Incomplete Subacute Transverse Myelitis Following Vaccination With Pfizer-BioNTech COVID-19 mRNA Vaccine: A Case Report
Bibliographic record
Abstract
In response to the coronavirus disease 2019 (COVID-19) pandemic, rapid development, clinical testing, and regulatory approval of vaccines occurred. The tozinameran COVID-19 vaccine is the first mRNA vaccine approved for use in humans. Transverse myelitis is a rare inflammatory disorder of the spinal cord that is associated with traditional vaccinations. There are rare case reports describing an association between mRNA vaccines and transverse myelitis. Herein, we describe a case of transverse myelitis following mRNA vaccination. A healthy 26-year-old woman developed saddle anesthesia, numbness, and allodynia in the S1-S4 distribution within three days of receiving the first dose of tozinameran COVID-19 vaccine. She had decreased sensation to pinprick, temperature, and light touch in S1-S4 distribution and a positive Rhomberg test. An MRI brain and spine demonstrated a short segment T2 hyperintense and diffusely enhancing lesion at T5. Cerebrospinal fluid studies demonstrated pleocytosis and elevated IgG index. A five-day course of IV methylprednisolone resulted in minimal improvements in her symptoms. Stage III clinical trials may be underpowered to detect more rare adverse effects such as transverse myelitis. Therefore, it is imperative to have ongoing surveillance and reporting of adverse events associated with COVID-19 vaccines to ensure transparency with regard to potential risks to patients obtaining the vaccine and algorithms in place for detection and urgent treatment if required. Nonetheless, the safety and efficacy of vaccination against COVID-19 are well established and greatly outweigh any potential risks associated with the vaccine. Given the individual, societal, and global health benefits of vaccination we strongly advocate for ongoing vaccinations against COVID-19.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".