Investigating Gender Differences in Canadian Plastic Surgery Online Patient Education
Bibliographic record
Abstract
BACKGROUND: The public interest and demand for cosmetic surgery is growing in North America. As practices continue to advertise cosmetic procedures, male consumers must also be given fair consideration in a market targeted mostly towards women. OBJECTIVES: This study investigated the advertising trends of plastic surgery clinics to assess for prevailing gender differences among online Canadian plastic surgery practice advertising. METHODS: The 2021 College of Physicians and Surgeons directory for each province and territory was utilized to identify all practicing plastic surgeons. A systematic search with Google (Mountain View, CA) was conducted to analyze the websites of Canadian plastic surgery centers in the following manner: "[physician name] [province of practice]." RESULTS: A total of 209 websites and 13,838 images were identified and analyzed. Of these images, 12,386 (90%) were female and 1452 (10%) were male patients or models. Although only 20% had a male services page, 62% of all centers offered gynecomastia procedures. The most common procedures targeting men were blepharoplasty (95%), liposuction (93%), and abdominoplasty (93%). The Prairies region had significantly fewer websites with male-only pages compared with all other Canadian regions. CONCLUSIONS: Despite the increase in cosmetic surgery procedures for males, the market for cosmetic surgery procedures targeted towards males remains insignificant. An increase in the advertising of in-demand male cosmetic procedures can allow for a broader consumer market and a subsequent increase in benefits for plastic 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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".