Efficacy and Safety of RelabotulinumtoxinA Liquid Botulinum Toxin in the Treatment of Lateral Canthal Lines: Results From the Phase 3 READY-2 Study
Bibliographic record
Abstract
BACKGROUND: RelabotulinumtoxinA (RelaBoNT-A) is a complex-free, ready-to-use, liquid botulinum toxin A. OBJECTIVE: Efficacy/safety of RelaBoNT-A treatment for lateral canthal lines (LCL). METHODS: Randomized adults received RelaBoNT-A (30 U/side; n = 230) or placebo ( n = 73) during a 6-month, double-blind, Ph3 study (Relabotulinumtoxin Aesthetic Development Study-2 [READY-2]). Primary end points (Month 1, maximum smile) comprised: composite ≥2-grade responder rate using concurrent LCL severity investigator live assessment (LCL-ILA) and subject live assessment (LCL-SLA); LCL-ILA 0 (none)/1 (mild) responder rate. Subject satisfaction and adverse events were also reported. RESULTS: Month 1 composite ≥2-grade responder rates were 51.8% (RelaBoNT-A) and 1.4% (placebo; ( p < .001). Month 1 none/mild LCL-ILA responder rates were 87.2% (RelaBoNT-A) and 11.9% (placebo; p < .001). Onset was reported Day 1 by 34%. At Month 6, LCL-ILA responder rates for RelaBoNT-A remained at 23.3% (none/mild) and 35.9% (≥1-grade improvement). Median return to baseline severity was 24.7 weeks; 64% (RelaBoNT-A group) had not returned to baseline at Month 6. RelaBoNT-A satisfaction was high through Month 6 (71%). Mild/moderate treatment-related adverse events occurred in 6.1% (RelaBoNT-A) and 5.5% (placebo). CONCLUSION: RelaBoNT-A (60 U) treatment provided statistically significant improvement of moderate-to-severe LCL. One-third of subjects reported onset within 1 day and improvements were maintained through Month 6. Treatment satisfaction was high. RelaBoNT-A was well tolerated.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".