Multicenter, Randomized, Parallel-Group Study of OnabotulinumtoxinA and Hyaluronic Acid Dermal Fillers (24-mg/mL Smooth, Cohesive Gel) Alone and in Combination for Lower Facial Rejuvenation: Satisfaction and Patient-Reported Outcomes
Bibliographic record
Abstract
BACKGROUND Patient-reported outcomes (PROs) are important assessment benchmarks after aesthetic procedures. Systematic studies of PROs have not been conducted in the lower face. OBJECTIVE To study satisfaction and other PROs after treatment of the lower face with onabotulinumtoxinA and a 24-mg/mL smooth, cohesive hyaluronic acid (HA) gel filler, alone or in combination. 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 were investigator- and participant-rated satisfaction and the participant Self-Perception of Age (SPA) and participant-rated Look and Feel of the Lips and Mouth (LAF) questionnaires. Participants maintained a 14-day diary to record severity of treatment site responses. RESULTS All treatments resulted in significant improvements from baseline at all end points and on all PRO measures. For all measures and most time points, the 24-mg/mL cohesive gel treatment groups experienced greater improvements than onabotulinumtoxinA alone. Participant-rated severity of treatment-related reactions was mainly mild and transient. CONCLUSION OnabotulinumtoxinA and 24-mg/mL cohesive HA gel treatments, used alone or in combination for lower face rejuvenation, resulted in significant improvement in investigator- and participant-reported outcomes. The authors acknowledge statistical support from Ethica Clinical Research, Inc. (Montréal, Quebec, Canada). 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. Funding for editorial support was provided by Allergan, Inc., Irvine, CA. Writing and editorial assistance was provided by Paula G. Davis, PhD, of Health Learning Systems, Parsippany, NJ. The authors received research grant support from Allergan, Inc. for this study and for manuscript preparation.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".