Incidence and treatment of delayed‐onset nodules after VYC filler injections to 2139 patients at a single Canadian clinic
Bibliographic record
Abstract
BACKGROUND: Data suggest that hyaluronic acid (HA) fillers using VYC technology have a higher incidence of delayed-onset nodule development at facial injection sites than earlier HA products. OBJECTIVE: To assess the incidence of delayed-onset nodules with VYC products based on a single experienced injector. METHODS AND MATERIALS: Patients with delayed-onset nodules after injections with VYC-20L, VYC-17.5L, and VYC-15L were identified by retrospective chart review. RESULTS: Since 2010, 2139 patients received injections from the same physician with combinations of VYC-20L (57.6% of patients; 2.4 syringes/patient), VYC-17.5L (23.9%; 1.5), or VYC-15L (18.5%; 1.5). Seven female patients (mean age, 62 years) developed delayed-onset nodules for an overall incidence of 0.33%. A potential inflammatory trigger (reported by 6 patients) occurred 1-168 days prior to nodule development. Nodule biopsy in 1 patient confirmed a foreign-body granuloma. The most effective treatment incorporated prednisone with or without hyaluronidase, and in 2 patients, nodules resolved spontaneously. The incidence of delayed-onset nodules was not associated with injection technique or amount of product used. CONCLUSION: VYC-associated incidence of delayed-onset nodules (0.33%) was lower than earlier estimates from previous studies. In the current analysis, VYC-15L had a rate of delayed reactions comparable with non-VYC products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".