Radiesse Rescue: A Preliminary Study for a Simple and Effective Technique for the Removal of Calcium Hydroxyapatite–Based Fillers
Bibliographic record
Abstract
BACKGROUND: Radiesse, or calcium hydroxyapatite (CaHA), is a semipermanent, biodegradable injectable filler that provides immediate aesthetic improvement, while also stimulating neocollagenesis for biological effects. The physical properties of CaHA make it difficult, if not impossible, to remove. Unlike some hyaluronic acid-based fillers, CaHA cannot be easily dissolved. OBJECTIVES: The aim of this study was to present a simple and reliable technique for debulking and removing excess CaHA in the event of nodule formation, vascular compression, or overcorrection. METHODS: An 18-gauge needle was used to make an incision near the filler excess. A 1-mm-diameter grater-type microliposuction cannula (Lipocube, Inc.; London, UK) was attached to a 5- to 10-mL syringe under negative pressure. The cannula under syringe suction was used in a back-and-forth reaming motion beginning in the base of the material and gradually moving toward the surface until the desired effect was achieved. Identification of the CaHA can be established and removal confirmed with ultrasound (Clarius, Inc.; Vancouver, BC, Canada) if available. RESULTS: Although nodules, excess material and vascular compression secondary to CaHA are very rare occurrences, 4 patients were treated with excess material and/or nodules. In 1 patient, the prominent cheek filler was reduced and a 1-cm nodule that was palpable in the buccal region was eliminated. In the other 3 other patients, the palpable excess material was easily removed, in 2 at the time of injection and in the third, 6 months later. No patient required repeat treatment or replacement. CONCLUSIONS: This removal technique has been shown to adequately remove excess CaHA filler but is not applicable to inadvertent intravascular injection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".