The Suitability of a Large Particle Hyaluronic Acid Filler for the Treatment of Temporal Hollowing
Bibliographic record
Abstract
BACKGROUND: Hyaluronic acid (HA) fillers may be manufactured to have distinctive physical properties that optimize their use for specific indications. Fillers manufactured with large gel calibration (particle size; HA-V) may be particularly suitable for volumizing large surface areas such as the temporal hollows. OBJECTIVE: To investigate the safety and effectiveness of HA-V for the treatment of temporal hollows. MATERIALS AND METHODS: A prospective, open-label, single-cohort, clinical trial was conducted. Twenty-six women who presented with bilateral temporal hollows at baseline were recruited. All subjects received treatment with HA-V and were observed at 4 to 5 in-person visits over 16 weeks. Subjective and objective measures of safety and efficacy parameters were collected through 2- and 3-dimensional imagery, questionnaires/scales (i.e., subject satisfaction, global aesthetic improvement, temporal hollowing severity), and adverse event diaries. RESULTS: To achieve optimal correction, the investigator used an average of 1.70 syringes per subject, per side. All treatments were performed using a bolus injection technique to place the product on the periosteum (bone) of the temporal region. Following optimal correction, all subjects (100%) displayed improvement in their global aesthetic appearance, and 25 of 26 subjects (96.15%) displayed ≥1 grade improvement on the temporal volume scale. Subject satisfaction was high, with 91.3% of subjects being satisfied with the appearance of their temporal regions following optimal correction. CONCLUSION: In this pivotal trial, HA-V was evidenced to have an excellent safety profile and proven efficacy up to 16 weeks, making it a suitable HA filler for volumization of the temporal region.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".