Bibliographic record
Abstract
BACKGROUND: Until recently, deep facial sculpting was exclusively the domain of surgical interventions. Recent advances in the available array of dermal and subdermal fillers combined with an esthetic appreciation by both surgeons and nonsurgeons alike of the positive effect of filling the volume-depleted face have led to an expansion in the indications for the use of soft tissue augmenting agents. METHOD: Subdermal support of the lateral two-thirds of the brow, the nasojugal fold, the malar and buccal fat pads, the lateral lip commissures, and the perioral region, including the pre-jowl sulcus, all restore youthful facial contour and harmony. An important advance in technique is the subdermal rather than the intradermal injection plane. RESULTS: "Instant" facial sculpting giving a brow-lift, cheek-lift, lip expansion, and perioral augmentation is possible using modern soft tissue augmenting agents. The softer, more relaxed appearance contrasts to the somewhat "pulled" appearance of subjects who have had surgical overcorrections. Treatments can be combined with botulinum toxin and other procedures if required. CONCLUSION: Newer advances in the use of fillers include the use of fillers injected in the subdermal plane for "lunchtime" facial sculpting. Using the modern esthetic filler compounds, which are biodegradable but longer lasting, subjects can have a "rehearsal" treatment or make it ongoing. Some individuals, such as those with human immunodeficiency virus (HIV)-related lipoatrophy or those who desire to obtain a longer-lasting effect, may elect to use a nonbiodegradable filling agent.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".