Brazilian Butt Lift Demand Trends: A Descriptive Study Across Major East Coast North American Cities
Bibliographic record
Abstract
This research study examines consumer interest in Brazilian Butt Lift (BBL) surgery across six major East Coast North American cities: New York, Miami, Boston, Philadelphia, Washington D.C., and Toronto. Using data from Google Keyword Planner between August 2023 and July 2025, the report tracks trends in search volume and year-over-year growth for BBL-related keywords, including “safe BBL,” “BBL cost + city,” and “best BBL surgeon near me.” New York and Miami continue to dominate in overall search volume, reflecting their long-standing reputation in the cosmetic surgery space. However, cities like Boston (+7.3%) and Washington D.C. (+6.5%) showed the fastest growth in consumer interest—indicating a shift toward emerging BBL markets and growing awareness around BBL safety and procedure outcomes. This descriptive trend analysis is designed to help plastic surgeons, clinics, and marketers align content strategies with real-time consumer behavior. The findings also support patient education efforts by identifying where demand for butt augmentation surgery is accelerating. Keywords: Brazilian Butt Lift trends, East Coast BBL demand, safe BBL, butt augmentation, BBL cost by city, BBL search analysis, plastic surgery marketing
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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 | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".