Safety and tolerability of onabotulinumtoxinA in the treatment of upper facial lines from global registration studies in 5298 participants: A meta-analysis
Bibliographic record
Abstract
Background Since its discovery as a facial aesthetic treatment >30 years ago, onabotulinumtoxinA has received worldwide approval for dynamic upper facial line treatment. Objective Meta-analysis examining the safety of onabotulinumtoxinA for treatment of glabellar lines (GL), crow's feet lines (CFL), and forehead lines (FHL). Methods Participants ( N = 5298) with moderate to severe GL, CFL, or FHL at maximum contraction received onabotulinumtoxinA or placebo in 1 of 18 registration studies (14 double-blind, placebo-controlled [DBPC]; 1 double-blind; 3 open-label). Adverse events (AEs) were analyzed by descriptive statistics and fixed-effects meta-analysis. Results In the overall double-blind placebo-controlled (DBPC) population, AEs were reported in 1443 (42.1%) and 486 (35.8%) participants in the onabotulinumtoxinA ( n = 3431) and placebo ( n = 1359) groups, respectively. Serious AEs were reported in 54 (1.6%) and 17 (1.3%) participants; 1 (spontaneous abortion) was considered possibly treatment related by the investigator. Using fixed-effects statistical meta-analysis, AEs of interest that were found to be statistically higher for onabotulinumtoxinA than placebo in the DBPC population were eyelid ptosis, eyelid sensory disorder, skin tightness, brow ptosis, eyelid edema, and facial pain ( P ≤ .05). Limitations Retrospective, ad hoc analysis. Conclusion This meta-analysis confirms the onabotulinumtoxinA safety profile for GL, CFL, and FHL treatment, with no new onabotulinumtoxinA-associated AEs.
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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.046 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".