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 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".