Hyaluronic acid soft tissue filler delayed inflammatory reaction following COVID‐19 vaccination – A case report
Bibliographic record
Abstract
BACKGROUND: The use of hyaluronic acid soft tissue fillers in aesthetic medicine exploded in recent years for many reasons, including being relatively safe. Incidence of delayed inflammatory reactions (DIRs) to hyaluronic acid soft tissue fillers range between 0.3% and 4.25%. These reactions are mediated by T-lymphocytes and can be triggered by flu-like illnesses, including SARS-CoV-2 infection. Vaccination may also induce hypersensitivity. AIM: In this case report, we present two cases of delayed reaction after hyaluronic acid soft tissue filler treatment of the tear trough area and following mRNA vaccination against SARS-Cov-2, also known as COVID-19, months later. PATIENTS: A 39-year old female who previously had her tear trough area treated with hyaluronic acid soft tissue filler developed swelling days after getting the mRNA Pfizer-BioNTech COVID-19 vaccine. Another patient, a 61-year-olf female, developed intermittent facial swelling in areas previously treated with hyaluronic acid soft tissue fillers days after receiving her first dose of the mRNA Pfizer-BioNTech COVID-19 vaccine. RESULTS: As demonstrated in our case report, vaccination against COVID-19 may induce DIRs in patients who previously had hyaluronic soft tissue fillers. CONCLUSION: Delayed inflammatory reactions to hyaluronic acid soft tissue fillers are uncommon and usually self-limited, with frequent spontaneous resolution. However, considering the ongoing pandemic and the worldwide demand for vaccines against COVID-19, the aesthetic providers should be conscious of the risks posed by the interaction of such vaccines in patients who previously had or seeking hyaluronic acid soft tissue filler injections.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".