Reduction of masseter muscle prominence after treatment with onabotulinumtoxinA: Primary results from a randomized phase 2 study
Bibliographic record
Abstract
BACKGROUND: OnabotA is used to treat masseter muscle prominence (MMP). OBJECTIVE: To assess the safety and efficacy of OnabotA for MMP in a randomized study. METHODS: This 12-month, multicenter, double-blind, placebo-controlled, phase 2 study randomized adults (18-50 years of age) with marked/very marked bilateral MMP (≥4 on the Masseter Muscle Prominence Scale [MMPS]) to OnabotA (24, 48, 72, or 96 U) or placebo; retreatment occurred at day 180 if MMPS ≥4. Lower facial volume at day 90 was measured using Vectra 3-dimensional photography. Safety assessments included computed tomography and dental exams. Evaluations occurred monthly through day 360. RESULTS: Among 187 randomized subjects, significant lower facial volume reductions and percentage of responders (MMPS grade ≤3) were greater with OnabotA versus placebo at day 90 (P < .001 and ≤.008, respectively). Similar efficacy was observed with retreatment. No dose-related safety trends or clinically relevant changes in the mandible or teeth occurred. Localized impact on smile was reported with 96 U OnabotA (n = 4). LIMITATIONS: Limited sample size per individual treatment group. CONCLUSION: OnabotA administered in 1 or 2 treatments over 1 year was associated with significant reductions in masseter muscle volume and MMP severity, with an acceptable safety profile.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".