Consensus Recommendations for Soft-Tissue Augmentation with Nonanimal Stabilized Hyaluronic Acid (Restylane)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The American Society for Aesthetic Plastic Surgery recently reported that there were nearly 12 million cosmetic procedures (2.1 million surgical and 9.7 million nonsurgical) performed in the United States in 2004. Almost 900,000 of the nonsurgical procedures were soft-tissue augmentation procedures using hyaluronic acid fillers. Restylane (Medicis Aesthetics, Inc., Scottsdale, Ariz.), nonanimal stabilized hyaluronic acid, was approved for use in the United States in December of 2003. Although the use of all fillers increased from 2003 to 2004, use of hyaluronic acid fillers increased nearly 700 percent. The dramatic increase in all cosmetic procedures reflects the growing trend, especially with increasing job competition, to maintain a youthful lifestyle and appearance. Basic recommendations for aesthetic use of Restylane were established based on short- and long-term efficacy and safety studies (Medicis Aesthetics, package insert). With the widespread and growing use of Restylane, a cross-sectional panel of experts with extensive clinical experience, including cosmetic dermatologists and surgical specialists (cosmetic, plastic, and ocular), convened to develop consensus guidelines for the use of Restylane. This supplement reviews the aesthetic affects of aging on the face, the role of fillers in facial soft-tissue volume replacement, and general principles for the use of Restylane, including patient comfort and assessment techniques. Specific recommendations for Restylane use in each potential target area, including type of anesthesia, injection techniques, volume for injection, use in combination with other procedures, and expected longevity of corrections, are provided. Techniques for optimizing patient outcomes and satisfaction and for minimizing and managing expected problems and potential complications are described.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it