Weight-Loss Surgery & Tummy Tuck Demand Across 8 Major North American Cities (2023–2025)
Bibliographic record
Abstract
Overview. This observational report quantifies consumer interest in weight-loss surgery (bariatric surgery, sleeve gastrectomy, gastric bypass) and tummy tuck (abdominoplasty) across eight large North American cities—New York, Los Angeles, Toronto, Chicago, Houston, Dallas, Phoenix, and Tampa. Using Google Keyword Planner data from Aug 2023–Jul 2025, we aggregated average monthly search volume and computed year-over-year (YoY) change to profile regional demand. Methods. City-level keyword groups were standardized (e.g., “weight loss surgery,” “bariatric surgery,” “sleeve gastrectomy,” “tummy tuck,” “abdominoplasty,” “best tummy tuck surgeon + [city]”). Metrics reported include average monthly searches by category and YoY % change. No inferential statistics were performed; this is a descriptive trends analysis. Key findings. Overall search interest is highest in New York and Los Angeles. Tummy tuck demand clusters in Sun Belt markets (Dallas, Houston, Phoenix, Tampa), while weight-loss surgery shows the fastest YoY growth in Toronto and Phoenix. These patterns suggest staged care—medical weight reduction followed by body contouring—remains a common pathway. Use. Results can guide clinic capacity planning, patient education, and localized SEO strategy. The record includes a publication-ready table and a combined bar + line chart for reuse with attribution. For consumer guides and surgeon discovery, visit https://www.aestheticmatch.com/. License: CC BY 4.0.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".