Precision in Midfacial Volumization Using Ultrasound-Assisted Cannula Injections
Bibliographic record
Abstract
BACKGROUND: Soft-tissue filler injections performed with a cannula are perceived to be less precise because of the length of the instrument and the blunt tip, which can deviate in any direction. Midfacial needle injections are favored despite the increased risk for intraarterial product placement. The objective of this study was to demonstrate that ultrasound-assisted cannula injections of the midface result in precise, safe, and effective volumization procedures. METHODS: Midfacial injections with a 22-G cannula were performed in 188 midfaces of 94 healthy volunteers [86 women; age, 53.05 (9.9) years; 23.63 (2.1) kg/m 2 ] under ultrasound-assisted guidance. Precision (ie, administration of product in the same plane as the location of the cannula tip), safety (ie, rate of adverse events), and aesthetic outcome (rated by the patient and the treating physician) were assessed. RESULTS: In 100% of cases, the product was applied into the desired deep midfacial fat compartment, and the product did not migrate into more superficial layers during the injection process or at any follow-up visit. There was a statistically significant ( P < 0.001) improvement in midfacial volume loss and the aesthetic outcome was rated as very much improved. No adverse events were reported throughout follow-up. CONCLUSIONS: Real-time imaging allows for visual feedback during cannula advancement and injection procedures in the midface and can help practitioners achieve safer aesthetic outcomes. It is hoped that practitioners decide to use a cannula more frequently for midfacial volumization, given the results presented in this article. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".