OnabotulinumtoxinA Treatment for Moderate to Severe Forehead Lines: A Review
Bibliographic record
Abstract
With onabotulinumtoxinA approved for the treatment of glabellar and crow's feet lines and, most recently, for forehead lines (FHL), it is possible to simultaneously treat multiple areas of the upper face that are of high concern and treatment priority for aesthetically oriented individuals. This review aims to present key insights on the use of onabotulinumtoxinA for the treatment of moderate to severe FHL. METHODS: Double-blind, placebo-controlled registration trials of onabotulinumtoxinA for the treatment of FHL were included. Using findings from 3 such published studies, we discuss key concepts and clinical experience for the treatment of moderate to severe FHL with onabotulinumtoxinA (20 U in the frontalis and 20 U in the glabellar complex, with/without 24 U in crow's feet lines), including injection pattern, dose selection, efficacy and safety data, and considerations for patient selection. RESULTS: < 0.0001), and results were maintained through 3 cycles of onabotulinumtoxinA. CONCLUSIONS: OnabotulinumtoxinA treatment also resulted in high patient satisfaction rates. The incidence of eyebrow and of eyelid ptosis was low, and no new safety signals were detected. OnabotulinumtoxinA is safe and effective and an appropriate option for patients with moderate to severe FHL encountered in clinical practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".