Uždelstos uždegiminės reakcijos į hialurono rūgšties dermos užpildus po virusinės infekcijos
Bibliographic record
Abstract
Relevance of the problem and aim. Hyaluronic acid (HA) dermal fillers have gained worldwide popularity in aesthetic medicine, however delayed inflammatory reactions (DIRs) have been reported following virus infections and vaccinations. This study aimed to review and analyze reported cases of DIRs in HA treated patients after virus exposure. Materials and methods. This systematic review was conducted in accordance with PRISMA guidelines. Articles were searched between October 19th 2024 and November 2024 using PubMed, ScienceDirect, ResearchGate, ClinicalKey, and Google. Keywords used ”Hyaluronic Acid”, ”Dermal Filler” and ”Delayed inflammatory reaction”. Inclusion criteria included, full-text access, written in english language and Case reports involving patients treated with Hyaluronic acid dermal filler in the facial region. Six studies (four case series, two case reports) met the inclusion criteria. Risk of bias was assessed using the Newcastle-Ottawa Scale. Results. All of the included studies showed DIRs developing post-COVID-19 infection, vaccination, or influenza-like illness. Symptoms included edema, erythema, and tenderness, with onset ranging from hours to weeks. Treatments varied, with corticosteroids and hyaluronidase being most common, though some cases resolved spontaneously. Conclusion. Virus infections and vaccinations can trigger DIRs in HA-treated patients. The most common treatments used were corticosteroids and hyaluronidase, although some cases resolved spontaneously. However such DIRs highlights the need for standardized protocols and treatment techniques for addressing these reactions.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".