Hyaluronidase for Treating Complications Related to HA Fillers: A National Plastic Surgeon Survey
Bibliographic record
Abstract
Background: Hyaluronic acid (HA) fillers have become a popular modality to address changes in the ageing face. There are many described indications of hyaluronidases in aesthetic medicine which include their use in the management of HA-associated complications. To better understand the current practice patterns, we surveyed Canadian plastic surgeons on their use of hyaluronidases. Methods: With the approval of the Canadian Society of Plastic Surgeons, an electronic survey was emailed to members. A total of 350 surveys were distributed and 98 surveys were completed for a response rate of 28%. Results: Approximately half (48%) of the survey respondents used HA fillers in their practice. Skin testing for hypersensitivity reactions was performed by less than 10% of hyaluronidase users. Nearly all respondents used hyaluronidase for filler over-correction (95.5%) and asymmetry (86.4%). Over half of the respondents have used hyaluronidase for inflammatory or infectious nodules and the Tyndall effect. Other reported applications included restoration of vascular compromise, and one respondent reported using hyaluronidase for assisting with haematoma resolution. When compared with the most recent guidelines, there was a wide range of doses used for common side effects and complications. Twenty-four percent of the respondents reported that their hyaluronidase formulation was prepared by a compounding pharmacy, and 20% of respondents who inject HA fillers did not stock hyaluronidase. Conclusion: There are many indications for hyaluronidase in aesthetic plastic surgery. Plastic surgeons should stock hyaluronidase and develop a specific plan in anticipation of adverse events. Although hyaluronidase is commonly used by plastic surgeons for over-correction and asymmetry, the dosages used in aesthetic practice is rather diverse and heterogeneous. When possible, plastic surgeons should perform allergy testing before hyaluronidase use.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".