Appreciation of the Vascular Anatomy of Aesthetic Forehead Reflation
Bibliographic record
Abstract
BACKGROUND: Worldwide, the brow is the most common facial site to receive aesthetic treatment. However, the forehead above the brows has been comparatively less well studied with respect to both neuromodulators and fillers. Age-related remodeling of the forehead with loss of facial bone has been demonstrated on detailed radiographic studies. Concurrent loss of facial fat deposits also adds to the volume depletion. The resulting shallow scalloped depressions in the central 2/3 of the forehead give a tired and aged appearance as do the deep etched horizontal forehead lines which are often associated. Temporal hollowing may be an important associated feature. METHODS: Combination treatment of the upper face with neuromodulators to elevate the brows and diluted hyaluronic acid (HA) fillers to smooth the medial glabellar complex and reflate the forehead and temple has recently become a desired and popular treatment. Several techniques have been described in the literature. All these techniques are designed to allow forehead reflation with reduction of the possibility of vascular compromise. CONCLUSION: Avoidance of the supratrochlear and supraorbital vasculature with cosmetic filler injections is possible by insertion of needle or cannula into the subgaleal space above their transition from preperiosteal to subcutaneous level. Using this technique we have so far not seen any vascular compromise and we present this technique in the interest of patient safety.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".