Who Is Providing Aesthetic Surgery? A Detailed Examination of the Geographic Distribution and Training Backgrounds of Cosmetic Practitioners in Southern California
Bibliographic record
Abstract
BACKGROUND: In recent years, there has been a significant increase in the number of non-plastic surgeons performing cosmetic procedures. This has the potential to have an impact on the plastic surgery practitioner by increasing competition and bringing into question the assurance of patient safety. In this study, a demographic analysis was performed of providers of invasive and minimally invasive cosmetic treatments in the Southern California region. METHODS: Providers of minimally invasive aesthetic procedures were catalogued using the sales lists from the manufacturers of the hyaluronic acid fillers Juvéderm and Restylane. The data set was further analyzed to enumerate the providers of surgical treatments, with a focus on the provision of liposuction. Data were analyzed using Environmental Systems Research Institute ArcGIS to focus on the Southern California area. Physician board certification and training background were detailed exhaustively. RESULTS: In the 45,238-square mile area encompassing the San Diego/Los Angeles megalopolis, there are 1867 cosmetic practitioners offering hyaluronic acid injections. Of this number, 495 are trained in plastic surgery. In the same geographic region, there are 834 individuals offering liposuction. Practitioners are concentrated in downtown Los Angeles and San Diego, where the potential patient-to-provider ratios are 1088:1 and 1185:1, respectively. Interestingly, primary care physicians comprise the third largest group providing hyaluronic acid fillers and the fourth largest group of liposuction providers. CONCLUSIONS: The authors' data demonstrate that there are numerous non-plastic surgeons performing cosmetic procedures, especially in the field of minimally invasive therapies. In addition, there is a growing contingent of non-surgery-trained individuals providing surgical cosmetic treatments, especially liposuction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".