Identifying Levels of Competency in Aesthetic Medicine: A Questionnaire-based Study
Bibliographic record
Abstract
BACKGROUND: In 2022, the US experienced a significant increase in demand for minimally invasive aesthetic procedures, underscoring its rising acceptance amid an unregulated educational environment for practitioners. The absence of standardized educational pathways and quality control in aesthetic medicine, primarily provided by nonacademic institutions, highlights a critical need for establishing educational standards to ensure practitioner competence and patient safety. OBJECTIVES: The aim of this study was to identify levels of competency for the aesthetic practitioner and necessary achievement milestones during the educational path from novice to expert injector. METHODS: A total of n = 386 international study participants responded to an online questionnaire regarding their experience in aesthetic medicine practice. The questionnaire comprised 58 questions focusing on professional data, the perceived difficulty of injection, and risk for the occurrence of adverse events for specific facial regions in soft tissue filler and toxin injections. RESULTS: Regardless of medical specialty and experience level, averages of 3.85 (1.8) years, 786.4 (2628) filler injections and 549.9 (1543) toxin injections were estimated to progress from novice to advanced injector, while averages of 6.10 (3.7) years, 1842.2 (4793) filler injections, and 1308.5 (3363) toxin injections were estimated to advance from advanced to expert injector. The nose and the perioral region have been ranked as the facial regions where it is most difficult to achieve a perfect aesthetic outcome and with the greatest risk for the occurrence of adverse events for filler and toxin injections, respectively. CONCLUSIONS: In this study we establish an educational framework in aesthetic medicine by defining the progression from novice to competent and expert injector levels, suggesting 4 years of practice and over 790 filler and 550 neuromodulator injections for competence, and at least 6 years with 1840 filler and 1310 neuromodulator injections for expertise. We also identify critical facial regions for targeted treatments by different expertise levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".