Intracorporeal evaluation of hyaluronic acid fillers with varied rheological properties and correlations with aesthetic outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it