Evolution of Facial Aesthetic Treatment Over Five or More Years
Bibliographic record
Abstract
BACKGROUND: Little information exists on how facial aesthetic treatments are incorporated into aesthetic regimens. OBJECTIVE: Assess the evolution of facial aesthetic treatments in patients receiving long-term continuous onabotulinumtoxinA treatment. METHODS: This international retrospective chart review included patients with ≥5 years of continuous onabotulinumtoxinA treatments including ≥1 glabellar lines treatment/year. Charts were reviewed for facial areas treated, number of treatments, doses/treatment visit, concomitant aesthetic procedures, and onabotulinumtoxinA-related adverse events. RESULTS: Data were collected from 5,112 onabotulinumtoxinA treatment sessions for 194 patients over an average of 9.1 years. Dosing was relatively stable over time; however, interinjection intervals increased. Glabellar lines' treatment temporally preceded crow's feet lines and forehead lines' treatment. A majority of patients (85%) also received treatment with fillers. Cumulative increases in onabotulinumtoxinA treatments occurred over time and by facial area corresponding with increases in treatments with injectable fillers, energy-based devices, and prescription topical creams. The longer the patients were treated, the younger they perceived themselves to look. Rates of adverse events were low. CONCLUSION: OnabotulinumtoxinA treatment evolved over time, coinciding with growth of the facial aesthetics market. Additional treatment modalities were added as complements to onabotulinumtoxinA. Long-term continuous onabotulinumtoxinA injections are an important component of contemporary facial aesthetic treatment regimens.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".