Plastic Surgery Medical Tourism in Colombia: A Review of 658 International Patients and 1,796 Cosmetic Surgery Procedures
Bibliographic record
Abstract
BACKGROUND: Cosmetic surgery is at the forefront of a $60 billion medical tourism industry. Patients are now able to research options globally through the internet, and increasing numbers are seeking improved service, quality, and value through surgery overseas. This study examines 658 consecutive patients receiving 1,796 cosmetic surgery procedures at a private plastic surgery practice in Cartagena, Colombia. METHODS: We retrospectively reviewed the medical records of 658 consecutive international patients receiving cosmetic surgery at a private plastic surgery practice in Cartagena, Colombia. RESULTS: Patients traveled to Colombia from 34 different countries spread across 6 continents. Ninety percent of patients came from North America. Patients from the United States represented 38 states and the District of Columbia, and Canadian patients represented 7 provinces. Eighty-three percent of patients were women and 90% were between the age of 20 and 54. The 658 patients in this study had a total of 1,796 cosmetic surgery procedures, involving 5,456 surgical sites. Seventy-two percent of patients received combination procedures with an average of 2.7 procedures per patient. Ninety-nine percent of cases were performed under general anesthesia and 100% were performed in an accredited hospital. CONCLUSIONS: Our data highlight trends defining plastic surgery medical tourism. Our data indicate that traveling for surgery is widely appealing, with women and men from diverse backgrounds traveling to Colombia from all over the world for a variety of procedures.
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".