Bibliographic record
Abstract
BACKGROUND: This article is intended to provide practical information on the use of Juvéderm Ultra and Juvéderm Ultra Plus injectable gel fillers and to highlight differences between the injection techniques and uses of these fillers and those of gel-particle suspensions. The author's purpose was to provide early information to clinicians who perform aesthetic surgery. METHODS: The author discusses the properties of the Juvéderm family of products, compares these with particulate gel fillers, and makes recommendations for achieving optimal outcomes with Juvéderm as well as dermal fillers in general. RESULTS: When correcting relatively fine and superficial lines, or for first-time treatment of the lips, Juvéderm Ultra is preferred. Juvéderm Ultra Plus is useful for correction of deeper wrinkles and folds. Cohesive, homogenous gel fillers such as Juvéderm require gentle, gradual injection, which facilitates very accurate delivery to the site while minimizing the risk of disengagement of the needle from the Luer lock hub of the syringe. These fillers appear to remain in the desired location and do not flow away from the injection point into unintended areas, so that a somewhat smaller amount of filler is required for a given degree of correction. Both the Juvéderm preparations tend to blend in with the treated area in a shorter period of time compared with gel-particle suspensions. Finally, cohesive gels appear to be associated with a lesser degree of swelling in the 24-hour period after the procedure, particularly in the lips. CONCLUSIONS: Juvéderm Ultra and Juvéderm Ultra Plus are new-generation hyaluronic acid fillers used to treat superficial lines and deeper folds, respectively. They require administration by gentle, gradual, and precise injection.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".