Botulinum toxin for cosmetic treatments in young adults: An evidence‐based review and survey on current practice among aesthetic practitioners
Bibliographic record
Abstract
BACKGROUND: Botulinum toxin A (BoNTA) treatments are popular worldwide. Young adults, less than 41 years of age, are an important cohort of patients seen in practice, and the optimal dosage of BoNTA in this age group remains poorly defined. AIMS AND OBJECTIVE: To determine the optimal dosing of BoNTA across different age-matched cohorts by reviewing the literature and to evaluate current BoNTA practices among aesthetic practitioners when treating younger versus older adults. METHODS: An evidence-based literature review was performed to evaluate the current evidence on BoNTA injections for cosmetic indications in millennials (born between 1981 and 1996) and young adults. A cross-sectional online survey was distributed to aesthetic practitioners to assess their current practice with BoNTA. RESULTS: There is a paucity of high-quality research on BoNT in millennials. Our literature review suggests different patterns of practice when treating younger adults. Specifically, our survey revealed that: (1) younger adults are receiving, on average, fewer units of BoNTA at the glabella, forehead, and crow's feet, (2) younger toxin-naïve patients are also afraid to look frozen or unnatural, and (3) wrinkles prevention was the most common reason for seeking treatment in younger patients. CONCLUSION: The literature review supports the efficacy of BoNTA for correcting wrinkles in millennials. Our findings provide further insight into the dosing pattern of cosmetic BoNTA and motivating factors for seeking treatments in young adults. This valuable information will help practitioners with treatment planning when seeing patients from different age cohorts. An individualized approach and a lower dosage of BoNTA in toxin-naïve younger adults are recommended.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".