Full-face aesthetic treatment with onabotulinumtoxin A: Results from a retrospective real world analysis
Bibliographic record
Abstract
Background: Full-face aesthetic treatment with neuromodulators is common in routine practice but not widely analyzed in clinical studies. Methods: This was a retrospective, two-center chart review evaluating the effectiveness and safety of full-face onabotulinumtoxinA injections for the aesthetic treatment of adult females, all of whom had severe forehead lines at baseline. Patients received a total dose of 114 U: 64 U in the upper face using the on-label treatment pattern for glabellar, crow's feet, and forehead lines; 20 U per side in the jawline based on the recently proposed "toxin lift" method; and 10 U in the chin. Results: Thirty-three females were included (mean age: 42.5 ± 7.6 years). Physician-rated forehead line severity improved from "severe" to "mild" or "none" in all patients at 4 weeks, as assessed using the Facial Wrinkle Scale and Forehead Lines Grading Scale. All study participants showed greater facial symmetry, enhanced jawline contour, and high patient satisfaction (FACE-Q Satisfaction with Outcome, 83.9 ± 9.0; Satisfaction with Forehead and Eyebrows, 95.4 ± 5.7). Adverse events were minor and transient. Conclusions: Full-face onabotulinumtoxinA was effective in reducing facial lines and improving overall symmetry and jawline contour, with high levels of patient satisfaction, and a favorable safety profile. Level of Evidence: Level III.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".