OnabotulinumtoxinA and Hyaluronic Acid in Facial Wrinkles and Folds: A Prospective, Open-Label Comparison
Bibliographic record
Abstract
Background: OnabotulinumtoxinA and hyaluronic acid are effective in improving moderate to severe facial wrinkles and folds, with treatment selection traditionally based upon facial area. Objectives: This prospective, multicenter, open-label, crossover study evaluated physician-rated efficacy and patient-rated outcomes following moderate to severe facial wrinkles and folds treatment with onabotulinumtoxinA and hyaluronic acid. Methods: 152 subjects (25-65 years) were randomized (1:1) to a treatment-sequence of onabotulinumtoxinA/hyaluronic acid or hyaluronic acid/onabotulinumtoxinA, with initial treatment administered on day 1 and 6 additional visits: week 2 (touch-up); week 4 (crossover); week 6 (touch-up); and weeks 8, 12, and 24 (follow-up). Results: Between 92% and 100% of subjects in each treatment-sequence group exhibited at least some improvement from baseline at each study visit in the Physician Aesthetic Improvement Scale and the Objective Observer and Patient Global Assessments of Improvement, with no significant between-sequence differences. Subjects reported looking 3 to 6 years younger at each visit, with significant improvements in glabellar, lateral canthal, and horizontal forehead lines, and nasolabial folds. Treatments were well tolerated. Conclusions: OnabotulinumtoxinA and hyaluronic acid provide clinically meaningful improvements as rated by physicians, objective observers, and subjects, with clinical synergy in aesthetic effects and duration of response regardless of treatment administration order in subjects seeking improvement in moderate to severe facial wrinkles and folds.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".