Delayed-Onset Nodules Secondary to a Smooth Cohesive 20 mg/mL Hyaluronic Acid Filler
Bibliographic record
Abstract
BACKGROUND: The shift from 2- to 3-dimensional soft tissue augmentation has allowed the development of hyaluronic acid (HA) fillers, which are long lasting and also reversible. Delayed-onset inflammatory nodules have recently been reported with the use of HA fillers. OBJECTIVE: The authors document their experience with delayed-onset nodules after 3-dimensional facial injection of Juvéderm Voluma (HA-V) over 68 months. MATERIALS AND METHODS: The authors conducted a retrospective chart review of patients who were treated with HA-V between February 1, 2009, and September 30, 2014, to evaluate for delayed-onset nodules. RESULTS: Over 68 months, 4,702 treatments were performed using 11,460 mL of HA-V. Twenty-three patients (0.5%) experienced delayed-onset nodules. The median time from injection to reaction was 4 months, and median time to resolution was 6 weeks. Nine of the 23 (39%) had an identifiable immunologic trigger such as flu-like illness before the nodule onset. In the authors' experience, prednisone, intralesional corticosteroids, and hyaluronidase were effective treatments. CONCLUSION: Although delayed nodules are uncommon from HA-V (0.5%), it is important to be aware of this adverse effect and have a management protocol in place. It is the authors' opinion from the patients' responses and from the literature that these nodules are immune mediated in nature.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".