Delayed hypersensitivity reaction to cosmetic filler following two COVID-19 vaccinations and infection
Bibliographic record
Abstract
BACKGROUND: With ongoing COVID-19 vaccination schedules and the popularity of cosmetic fillers, it is important to examine and record associated adverse reactions to a more general audience of health care professionals. Case reports exist in subspecialty journals outlining reactions after SARS-CoV-2 infection and vaccination. This is one of the first cases published in Canada, and it highlights priorities and challenges faced by physicians in assessing and managing patients presenting with adverse reactions post vaccination. CASE PRESENTATION: We present a case of a 43 -year-old women with delayed type 4 hypersensitivity reaction to hyaluronic acid cosmetic filler triggered by COVID-19 mRNA vaccination. We outline the clinical presentation, diagnosis, complications, and treatment of a late inflammatory reaction to hyaluronic acid filler and highlight the treatment priorities for clinicians faced with similar presentations. CONCLUSION: The differential diagnosis of delayed onset nodules formation post filler injection is broad and includes redistribution of fillers, inflammatory reaction to biofilm, and delayed hypersensitivity reaction. As result, in order to make the right diagnosis, administer the appropriate treatment and achieve great cosmetic results, we highly recommend seeking expert opinion from dermatologist, plastic surgeon and allergist immunologist in a timely manner.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".