The Impact of Skin Care Product Sales in an Aesthetic Plastic Surgery Practice
Bibliographic record
Abstract
BACKGROUND: Despite significant growth of the global skin care market, many plastic surgeons do not offer skin care products through their aesthetic practice. However, skin care products represent a significant potential revenue stream for plastic surgeons, not only by generating revenue from product sales but by improving patient retention over time and, in turn, generating additional surgical and nonsurgical revenue. OBJECTIVES: The purpose of this study was to determine the financial implications that skin care sales can have for an aesthetic surgery practice. Our hypothesis was that patients making skin care purchases would generate higher non-skin care revenue than patients not purchasing skin care products. METHODS: A retrospective chart review was performed of all purchases made within a single aesthetic surgery practice during a 6-year period (2012-2017). Pre-tax revenue ($CAD) from each category was recorded for any patient who made a purchase during the study period. RESULTS: A total 3785 patients purchased skin care products, 5088 patients purchased nonsurgical treatments, and 3504 patients underwent surgery. Average patient spending was $720.73 (skin care), $1272.63 (nonsurgical), and $10,048.34 (surgery), respectively. Overall, patients who purchased skin care generated more revenue from the purchases of nonsurgical treatments and surgery than patients who did not purchase skin care products. CONCLUSIONS: Skin care sales not only generate revenue, but over time these patients spend more on nonsurgical and surgical treatments than patients who do not purchase skin care. Skin care is an important adjuvant to nonsurgical and surgical treatments that should be considered by all aesthetic surgeons.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".