Rates of Positive Aspiration Prior to Facial Hyaluronic Acid Filler Injections: Outcomes of a Multicenter, Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Before administering hyaluronic acid (HA) fillers, aspiration can be performed as a safety measure to determine whether the needle tip is located within a vascular structure. However, the efficacy and utility of aspiration have been questioned. A real-world evaluation would contribute to understanding how this technique is used globally. OBJECTIVES: The aim of this study was to determine the incidence (as a percentage) of positive pretreatment aspiration in a real-world setting. METHODS: An observational study with a cross-sectional design was conducted to evaluate the incidence of positive aspiration before facial HA injections. Investigators from 14 aesthetic/dermatologic practices in 9 different countries participated in the study. The active data collection period was 12 weeks. Data of all patients presenting to the participating clinics during the active data-collection phase, and who underwent HA injections to any region of the face, were included. The aspiration technique included slowly pulling back on the plunger of the syringe and holding it back for a minimum of 5 seconds, to allow proper time for flashback. RESULTS: Data from 5106 aspirations performed in 1007 individual patients were collected. In total, 35 cases (0.69%) of positive aspiration were recorded. However, there were significant differences in the incidence reported by investigators (range, 0%-6.72%). CONCLUSIONS: The results of this study can be used to assess the utility of pretreatment aspiration as a safety measure before performing HA filler injections, and contribute to the understanding of the effect of various factors on positive preinjection aspiration under clinical conditions.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".