Bridging the Gap: Sustained Treatment Effect of Glabellar Lines With Twice-A-Year Treatment With DaxibotulinumtoxinA
Bibliographic record
Abstract
BACKGROUND: To achieve natural-looking outcomes when treating dynamic lines with botulinum toxin (BoNT), retreatment must be timed such that the patient maintains a relatively constant aesthetic outcome. Although first-generation BoNT products require retreatment with 3- to 4-month frequency to avoid discontinuous correction, the average patient returns for treatment every 6 months, when these toxins have generally fully worn off. OBJECTIVE: To discuss the number of days a typical patient treated with daxibotulinumtoxinA for injection (DAXI) or legacy BoNT products will spend undertreated or uncorrected in a given calendar year. MATERIALS AND METHODS: Median time for maintaining glabellar lines in the "none" or "mild" severity range was compared for approved doses of onabotulinumtoxinA (ONA; 120 days) and DAXI (168 days). RESULTS: The average patient treated with 40U of DAXI every 6 months can expect to be uncorrected (with "moderate" or "severe" glabellar lines) for 14.5 days between visits compared with 61.5 days for 20U of ONA. CONCLUSION: An extended duration BoNT product can be expected to create greater consistency in aesthetic outcome and minimize the discontinuous correction commonly seen with first-generation BoNT products for patients treated twice a year, without requiring a change in patient behavior regarding visit frequency.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".