The six different injection techniques for the temple relevant for soft tissue filler augmentation procedures – Clinical anatomy and danger zones
Bibliographic record
Abstract
OBJECTIVE: The most promising facial region for inducing pan-facial effects is the temporal region. The temple displays signs of facial aging itself which include temporal volume loss and increased visibility of the temporal crest, the temporal vasculature, the lateral orbital rim, and the upper zygomatic arch. The objective of this article is to provide a detailed review of temple anatomy pertaining to routinely performed temporal injection techniques, their expected esthetic outcomes as well as the intendant advantages, disadvantages, and procedure pearls. MATERIALS AND METHODS: This narrative review is based on the clinical experience of the authors treating the temporal region for esthetic purposes. The postulated outcome of each technique was observed during the routine clinical practice of the authors. RESULTS: The temporal region is based on a bony platform consisting of the parietal, frontal, sphenoid, and temporal bones. The overlying soft tissues are arranged in layers which contain the temporal neurovascular structures. The temporal soft tissues consist of 10 parallel layers which vary in their thickness depending on age-related influences. Six different techniques will be addressed, which include subdermal and interfascial techniques for volumizing, low and high supraperiosteal techniques for volumizing, and supraauricular and temporal lifting techniques. CONCLUSION: This narrative provides a detailed anatomic overview of the temporal region and describes each commonly performed injection technique with respect to anatomy, esthetic outcome, as well as potential pearls and pitfalls. It is hoped that the description contained herein may guide esthetic practitioners toward safer and more natural outcomes when treating the face.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".