Treatment of Upper Facial Lines With DaxibotulinumtoxinA for Injection: Results From an Open-Label Phase 2 Study
Bibliographic record
Abstract
BACKGROUND: Simultaneous treatment of moderate-to-severe upper facial lines is reflective of real-world clinical practice. OBJECTIVE: To evaluate the efficacy and safety of daxibotulinumtoxinA-lanm for injection (DAXI) for simultaneous treatment of glabellar, forehead, and lateral canthal (LC) lines. METHODS: In this open-label, single-arm Phase 2 study, patients (48 enrolled, 94% completed, follow-up 24-36 weeks) received DAXI 40U (glabellar), 32U (forehead), and 48U (LC) lines. Key efficacy endpoints: percentages of patients achieving none/mild wrinkle severity (investigator-rated) for each upper facial line scale at Week 4. RESULTS: At Week 4, most patients achieved none/mild wrinkle severity (investigator-rated): glabellar (96%), forehead (96%), and LC (92%). Median times to loss of none/mild response (investigator- and patient-rated) among all patients were: 24.6 (glabellar), 20.9 (forehead), and 24.9 (LC) weeks; and 25.0, 24.0, and 28.1 weeks, respectively, among Week-4 responders. At Week 4, most patients reported improvements (Global Aesthetic Improvement Scale: 96%-98%) and high satisfaction rates (85%-98%). Five patients experienced treatment-related adverse events: injection-site erythema (3 patients/7 events), facial discomfort (2 patients/2 events), and headache (1 patient/1 event). No patients experienced eyebrow or eyelid ptosis. CONCLUSION: Simultaneous treatment of upper facial lines with DAXI was well tolerated and demonstrated high response rates, extended duration, and high patient satisfaction. CLINICAL TRIAL REGISTRY: https://clinicaltrials.gov/ct2/show/NCT04259086.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.004 | 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".