Facial Plastic Surgery Demand on the West Coast: Rhinoplasty & Facelift Trends Across 8 Major Cities (2023–2025)
Bibliographic record
Abstract
This observational report analyzes facial plastic surgery demand across eight major West Coast cities—Los Angeles, San Francisco, San Diego, Seattle, Vancouver, Portland, Oakland, and Long Beach—with a focus on rhinoplasty (nose jobs) and facelift procedures. Using Google Keyword Planner data from August 2023–July 2025, we compute average monthly search volume and year-over-year growth to profile city-level interest in facial rejuvenation and nasal reshaping. Los Angeles leads in total volume, while Seattle and Vancouver post the strongest growth, reflecting rising interest among younger, digitally engaged audiences. Methods, tables, and a bar-and-line chart are included for reuse. This report is part of AestheticMatch’s Health Analytics program to support providers, marketers, and researchers with geography-specific insights. Learn more at https://www.aestheticmatch.com/ and cite the Zenodo record when referencing figures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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; both teacher heads agree on what is shown here.
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".