Repeated OnabotulinumtoxinA Treatment of Glabellar Lines at Rest Over Three Treatment Cycles
Bibliographic record
Abstract
BACKGROUND: OnabotulinumtoxinA has demonstrated the ability to eliminate mild glabellar lines at rest; however, less is known regarding the effect of repeat treatment on more severe lines at rest. OBJECTIVE: To assess the effect of repeated onabotulinumtoxinA treatment for reduction of glabellar lines at rest. METHODS: Subjects 18 to 75 years old with at least mild glabellar lines at rest, as assessed by the validated Facial Wrinkle Scale (FWS) with photonumeric guide (score ≥ 1), received 3 treatments of 20 U onabotulinumtoxinA 4 months apart (N = 225). "Response" was defined as elimination of glabellar lines at rest (FWS score = 0) at any time point (Days 7, 30, 60, 90, and 120). Effect of treatment cycle on response was analyzed using repeated measures logistic regressions (p < .05). RESULTS: Most subjects were female (85%) and white (88%) (age range: 35-54 years). The likelihood of significant response was as follows: for all subjects combined (odds ratio [OR]: 1.31), for subjects with mild resting lines at baseline (OR: 1.49), and for older women (≥55 years) with mild resting lines at baseline (OR: 2.22). Of all subjects, 76% responded after 1 treatment, and 45% responded in all 3 cycles. CONCLUSION: Subjects repeatedly treated with onabotulinumtoxinA showed progressive improvement in glabellar lines at rest.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".