Intracorporeal evaluation of hyaluronic acid fillers with varied rheological properties and correlations with aesthetic outcomes
Bibliographic record
Abstract
BACKGROUND: Understanding the differences in soft tissue filler rheology and how these properties can impact clinical results is a fundamental concepts for any injector. This study aimed to assess the tissue integration characteristics of hyaluronic acid (HA) fillers manufactured with different technologies (Non-Animal Stabilized HA [HA-N] or Optimal Balance Technology [HA-O]) using ultra-high-frequency ultrasound. METHODS: Twelve female participants with mild-to-moderate midface volume loss and temporal hollowing were enrolled and treated with HA-N and/or HA-O. Participants were seen at five visits (screening/baseline [treatment], and Weeks 1 [optional touch-up], 4, 6, and 8 [follow-up visits]). Ultrasound was used to evaluate the degree of product integration. RESULTS: On ultrasound, HA-N presented with distinct borders, minimal tissue integration, and a capacity to displace tissues. Conversely, HA-O tended to spread horizontally within the same tissue plane and integrated within tissues. The volumizing capacity of the HA-O fillers was dependent on particle size. CONCLUSION: HA-N is suited for deep injections in areas such as the upper lateral cheek and under the muscle of the temporal region when a lifting effect is desired; HA-O is best suited for subcutaneous injections, in areas of dynamic movement or for patients with thin skin; and can be injected subcutaneously or supraperiosteally when a volumizing effect is desired.
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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) | 0.000 | 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) | 0.001 | 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".