Single-Center, Double-Blind, Randomized Study to Evaluate the Efficacy of 4% Lidocaine Cream versus Vehicle Cream During Botulinum Toxin Type A Treatments
Bibliographic record
Abstract
BACKGROUND: Botulinum toxin type A (BTX-A) injections are overwhelmingly safe and effective treatment in cosmetic treatment, but some patients are apprehensive about pain associated with injection. OBJECTIVE: To determine whether preprocedural application of lidocaine 4% topical anesthetic cream to the injection site will reduce pain on injection of BTX-A for the treatment of crow's feet. METHODS: Twenty-four participants receiving bilateral injections for crow's feet were enrolled. Subjects were randomized to one of four study groups. Prior to BTX-A injection, group 1 (n = 6) received lidocaine 4% cream on the right side of the face and vehicle cream on the left side of the face; group 2 (n = 6) received vehicle cream on the right side and lidocaine 4% on the left side; group 3 (n = 6) received lidocaine 4% on both sides; and group 4 (n = 6) received vehicle cream on both sides. RESULTS: We observed a statistically significant reduction in subject-reported procedural pain in participants pretreated with lidocaine 4% on both sides of the face compared with controls. CONCLUSION: Lidocaine 4% cream is effective in reducing the pain associated with BTX-A injection for crow's feet. We encourage further study to clarify the optimal use of topical anesthetics in the practice of cosmetic dermatology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".