Efficacy and Safety of OnabotulinumtoxinA Treatment of Forehead Lines
Bibliographic record
Abstract
BACKGROUND: Various onabotulinumtoxinA doses are effective in treating forehead lines (FHL), with a trend toward lower doses. OBJECTIVE: To evaluate efficacy and safety of onabotulinumtoxinA dose-ranging treatment of FHL when the frontalis area and glabellar complex are treated together. MATERIALS AND METHODS: Adults with moderate-to-severe FHL received onabotulinumtoxinA 40 U (FHL, 20 U; glabellar lines [GL], 20 U), 30 U (FHL, 10 U; GL, 20 U), or placebo. Response was assessed at weeks 1, 2, day 30, and monthly to day 180. Coprimary efficacy end points were investigator- and subject-assessed Facial Wrinkle Scale scores of none or mild (day 30). Patient-reported outcomes, onset/duration of effect, and adverse events (AEs) were evaluated. RESULTS: Responder rates (investigator/subject, respectively) were 40-U group, 91.2%/89.5%; 30-U group, 86.4%/81.4%; placebo, 1.7%/5.1%. OnabotulinumtoxinA resulted in significantly greater responder rates than placebo (p < .001). Adverse events were mild to moderate and similar between groups (most common AEs: nasopharyngitis [4.6%] and headache [4.0%]). CONCLUSION: Treatment of FHL with onabotulinumtoxinA 40 and 30 U (in frontalis and glabellar complex muscles) was tolerable, effective, and sustained. Both doses significantly reduced FHL severity; however, the 40-U dose demonstrated a trend toward greater sustained benefit and longer duration of effect versus the 30-U dose, with similar AE rates.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".