A case series of dermatomyositis following SARS-CoV-2 vaccination
Bibliographic record
Abstract
Background/Objective: The most significant adverse events following SARS-CoV-2 vaccination are myocarditis and pericarditis. Myositis and dermatomyositis have been reported following SARS-CoV-2 infection, but vaccine-induced dermatomyositis (DM) has not been reported. Our case series aimed to characterize new onset dermatomyositis or disease-related flares following SARS-CoV-2 vaccination. Materials and methods: A total of 53 patients from our institution with a new or pre-existing diagnosis of DM were recruited and consented. Phone interviews were conducted to obtain vaccination status and symptoms following vaccination. Electronic medical records were reviewed to extract age, sex, autoantibody profiles, comorbidities, immunomodulatory therapies, creatine kinase (CK) values, and SARS-CoV-2 vaccination dates from the provincial vaccination registry. For patients who reported disease flares, records were reviewed for the onset and nature of symptoms, extent of organ involvement and changes in immunomodulation. Results: On average, patients received 2.62 vaccine doses (range 1-3 doses). A total of 3 of 51 patients (5.88%) experienced dermatomyositis symptoms following vaccination. Two patients were newly diagnosed with dermatomyositis, one requiring hospitalization. Reported symptom onset following vaccination ranged from 1 to 30 days. Of note, all of these patients had normal CK values, even though there was muscle biopsy-confirmed myositis in one patient. Eight patients in the cohort (15.1%) had asymptomatic CK elevation (<1.5 X ULN). Conclusion: New onset dermatomyositis or flare up of pre-existing dermatomyositis may be a rare complication in SARS-CoV-2 vaccination although no studies can support a true correlation. Several pathophysiologic mechanisms are proposed.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".