NivobotulinumtoxinA in the Treatment of Lateral Canthal Lines Alone and With Concurrent Treatment of Glabellar Lines
Bibliographic record
Abstract
BACKGROUND: Botulinum neurotoxins in aesthetic medicine require reconstitution, which may cause administration errors. OBJECTIVE: To evaluate liquid nivobotulinumtoxinA treatment of lateral canthal lines (LCL) and glabellar lines (GL). MATERIALS AND METHODS: Participants with moderate-to-severe LCL with/without moderate-to-severe GL were enrolled in 2 double-blind randomized clinical trials. Study 002 participants received nivobotulinumtoxinA 24 U or placebo for LCL treatment. Study 006 participants received nivobotulinumtoxinA 24 U (LCL), nivobotulinumtoxinA 44 U (LCL+GL), or placebo. Each study allowed up to 2 additional doses. The composite primary end point was the proportion of participants achieving ≥2-grade improvement on the Facial Wrinkle Scale (FWS) by investigator and participant assessment at maximum smile; the coprimary end point was investigator- and participant-assessed FWS "none or mild" rating. RESULTS: At Day 30, significant responder rates versus placebo were observed for LCL in Study 002 (30.3% vs 2.6%, p < .001) and for LCL and LCL+GL in Study 006 (20.6% and 22.8% vs 0%, p < .001) for the composite end point. "None or mild" investigator/participant assessment of LCL (Study 002: 62.7%/56.5%; Study 006: 56.4%/43.6%) and LCL+GL (60.8%/47.3%) were significantly higher versus placebo ( p < .001). Headache was the most common adverse event. CONCLUSION: Liquid nivobotulinumtoxinA was effective and well-tolerated for treating moderate-to-severe LCL with/without GL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".