Multicenter, Randomized, Parallel-Group Study of the Safety and Effectiveness of OnabotulinumtoxinA and Hyaluronic Acid Dermal Fillers (24-mg/mL Smooth, Cohesive Gel) Alone and in Combination for Lower Facial Rejuvenation
Bibliographic record
Abstract
BACKGROUND Combination treatment with toxins and fillers is the standard regimen in facial rejuvenation. Systematic studies of botulinum toxin alone and in combination with hyaluronic acid (HA) have not, however, been conducted in the lower face. OBJECTIVE To evaluate safety and effectiveness and compare combination treatment with onabotulinumtoxinA and a 24-mg/mL smooth, cohesive HA gel filler with either treatment alone for rejuvenation of the perioral area and lower face in female subjects. METHODS Ninety female participants aged 35 to 55 were randomized to one of three groups: 24-mg/mL cohesive gel alone (n=30), onabotulinumtoxinA alone (n=30), or the combination (n=30). Effectiveness outcomes included perioral, lip fullness, and oral commissure assessments and scores on the Cosmetic Improvement and Global Aesthetic Improvement Scales. Adverse events were monitored throughout. RESULTS For all end points and most time points, subjects treated with onabotulinumtoxinA plus the 24-mg/mL cohesive gel had greater improvement from baseline than subjects treated with onabotulinumtoxinA or the 24-mg/mL cohesive gel filler alone. CONCLUSION Based on a range of end points, onabotulinumtoxinA and 24-mg/mL cohesive HA gel treatments are effective and safe when either alone or in combination to rejuvenate the lower face. Combination therapy is superior to either modality used alone. Drs. Alastair and Jean Carruthers are consultants and investigators and receive honoraria from Allergan, Inc. They are also consultants and investigators for Merz Pharmaceuticals and Solstice Neurosciences. Dr. Gary Monheit is a consultant and clinical investigator for Allergan, Galderma, Medicis, Merz Pharmaceuticals, and Revance Therapeutics.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".