Global Approaches to the Prevention and Management of Delayed-onset Adverse Reactions with Hyaluronic Acid-based Fillers
Bibliographic record
Abstract
BACKGROUND: Delayed-onset adverse reactions to hyaluronic acid (HA) fillers are uncommon but have received increased attention, particularly with regard to late-onset nodules. Globally, there is a need for comprehensive prevention and management strategies. METHODS: Experts with clinical practices in diverse regions of the world and extensive experience in managing complications related to HA fillers convened to propose and evaluate approaches to prevent delayed-onset adverse reactions after HA filler administration and manage late-onset nodules. RESULTS: The expert panel agreed to define delayed-onset adverse reactions as those presenting more than 4 weeks posttreatment, with swelling, induration, and nodulation being the most common clinical signs. The panel recommended 5 general key approaches for the prevention of delayed-onset reactions (patient selection, anatomic location of injection/product selection, aseptic technique, injection procedure/filler, and posttreatment care). Strategies recommended for managing late-onset nodules included oral antibiotics, oral steroids, nonsteroidal anti-inflammatory drugs if needed, hyaluronidase for noninflammatory nodules (recognizing the limitations and regional availability of this treatment), intralesional antibiotics, intralesional immunosuppressive drugs such as steroids and fluorouracil, and surgical excision as a last resort. The panel noted that late-onset nodules may vary in both clinical presentation and etiology, making them challenging to address or prevent, and stressed individualized treatment based on clinical presentation. Regional differences in aseptic protocols, antibiotic selection, and steroid formulations were described. CONCLUSION: Insights from global experts on approaches to prevent and manage delayed-onset adverse reactions following HA filler administration, including late-onset nodules, support clinicians worldwide in optimizing patient outcomes and safety.
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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 | 0.027 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".