Polymyalgia rheumatica and giant cell arteritis following COVID-19 vaccination: Results from a nationwide survey
Bibliographic record
Abstract
We conducted a national in-depth analysis including pharmacovigilance reports and clinical study to assess the reporting rate (RR) and to determine the clinical profile of polymyalgia rheumatica (PMR) and giant cell arteritis (GCA) in COVID-19-vaccinated individuals. First, based on the French pharmacovigilance database, we estimated the RR of PMR and GCA cases in individuals aged over 50 who developed their initial symptoms within one month of receiving the BNT162b2 mRNA, mRNA-1273, ChAdOx1 nCoV-19, and Ad26.COV2.S vaccines. We then conducted a nationwide survey to gather clinical profiles, therapeutic management, and follow-up data from individuals registered in the pharmacovigilance study. A total of 70 854 684 COVID-19 vaccine doses were administered to 25 260 485 adults, among which, 179 cases of PMR (RR 7. 1 cases/1 000 000 persons) and 54 cases of GCA (RR 2. 1 cases/1 000 000 persons) have been reported. The nationwide survey allowed the characterization of 60 PMR and 35 GCA cases. Median time to the onset of first symptoms was 10 (range 2-30) and 7 (range 2-25) days for PMR and GCA, respectively. Phenotype, GCA-related ischemic complications and -large vessel vasculitis as well as therapeutic management and follow-up seemed similar according to the number of vaccine shots received and when compared to the literature data of unvaccinated population. Although rare, the short time between immunization and the onset of first symptoms of PMR and GCA suggests a temporal association. Physician should be aware of this potential vaccine-related phenomenon.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".