Full‐face effects of temporal volumizing and temporal lifting techniques
Bibliographic record
Abstract
BACKGROUND: Most injection techniques utilizing hyaluronic acid-based soft tissue fillers have predictable outcomes at the location injected. However, the temporal region has been identified to have aesthetic effects beyond the temple. AIMS: To identify and quantify the panfacial aesthetic effects of three different temporal injection techniques. PATIENTS/METHODS: The medical records of nine female and five male Caucasian patients with a mean age of 50.9 ± 11.9 years were retrospectively reviewed for the effects of these techniques: supraperiosteal, interfascial, and subdermal. Panfacial effects were evaluated by the semiquantitative assessment of aesthetic scores for the temple volume, the temporal crest visibility, the lateral orbital rim visibility, the position of the eyebrows, the severity of lateral canthal lines, the midfacial volume, and the contour of the jawline. RESULTS: The supraperiosteal injection technique had the greatest influence on improving the temporal volume (25.0%), the temporal crest (33.3%), and the lateral orbital rim visibility (31.0%) scales but had no effects in other facial regions. The interfascial injection technique revealed good effects on improving temporal hollowing (23.3%) but had an even greater effect on the crow's feet (26.8%) and on the position of the eyebrow (33.3%). The subdermal injection technique had its greatest effects in the lower face by improving the contour of the jawline (26.8%) followed by the improvement of the lower cheek fullness scale (14.3%). CONCLUSION: Future injection algorithms could utilize all three injection techniques together as one multi-layer injection approach with a tailored proportion of each technique based on the aesthetic needs of the patient.
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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 | 0.000 | 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) | 0.003 | 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".