DAXXIFY<sup>TM</sup> (DaxibotulinumtoxinA-Lanm) for Injection, for Intramuscular Use.
Bibliographic record
Abstract
(daxibotulinumtoxinA-lanm) for intramuscular injection was recently approved for temporary improvement in the appearance of the moderate to severe glabellar lines (GLs) associated with corrugator and/or procerus muscle activity in adult patients. DaxibotulinumtoxinA for Injection (DAXI) includes a purified 150-kDA botulinum toxin Type A (BoNTA) formulated with a novel peptide excipient that is positively charged and helps to bind the neurotoxin to negatively charged neuronal membrane for a longer duration. The effectiveness of DAXI was evaluated in two phase 3 trials, SAKURA 1 and SAKURA 2, using a randomized, double-blind, placebo-controlled design. The primary endpoint (treatment success) was a composite clinical outcome (investigator and subjects) of ≥2-point improvement in severity of GLs at week 4. In SAKURA 1, the treatment success was 74% (148/201) in subjects treated with DAXI and 0% in subjects treated with placebo. In SAKURA 2, the treatment success was 74% (152/205) in subjects treated with DAXI and 0% in subjects treated with placebo. An open-label study, SAKURA 3, included 2,691 participants, who underwent three consecutive treatment cycles. These individuals were recruited from either SAKURA 1 or SAKURA 2 trials, or were new to the study and received DAXI. Treatment success proportions were 73.2%, 77.7%, and 79.6% across the three consecutive treatment cycles. The recommended dose is 40 units for the Glabellar-complex divided in traditional five intramuscular injections at five injection sites (medial and lateral corrugator bilaterally and one injection in the procerus muscle).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".