Practice Patterns in Primary Breast Augmentation: A 16-Year Review of Continuous Certification Tracer Data from the American Board of Plastic Surgery
Bibliographic record
Abstract
BACKGROUND: As part of the continuous certification process, the American Board of Plastic Surgery collects case data for specific tracer procedures in aesthetic surgery to assess practice improvement by the diplomates. These case-based data provide valuable information on national trends in clinical practice. The current study was performed to analyze practice patterns in aesthetic primary breast augmentation. METHODS: Breast augmentation tracer data were reviewed from 2005 to 2021 and grouped into an early cohort (EC), from 2005 through 2014, and a recent cohort (RC), from 2015 through 2021. Fisher exact tests and two-sample t tests compared demographic characteristics of the patients, surgical techniques, and complication rates. RESULTS: Patients in the RC were slightly older (34 versus 35 years; P < 0.001), more likely to have ptosis greater than 22 cm (20% versus 23%; P < 0.0001), less likely to smoke (12% versus 8%; P < 0.0001), and less likely to undergo a preoperative mammogram (29% versus 24%; P < 0.0001). From a technical standpoint, inframammary incisions have become more common (68% versus 80%; P < 0.0001), whereas periareolar incision use has decreased (24% versus 14%; P < 0.0001). Submuscular plane placement has increased (22% versus 56%; P < 0.0001), while subglandular placement has decreased (19% versus 7%; P < 0.0001). Silicone implants are most popular (58% versus 82%; P < 0.0001). Textured implant use increased from 2011 (2%) to 2016 (16%), followed by a sharp decline to 0% by 2021. Trends follow U.S. Food and Drug Administration approvals and warnings. CONCLUSIONS: This study highlights evolving trends in aesthetic breast augmentation over the past 16 years. The most common technique remains a smooth silicone prosthesis placed in the subpectoral plane through an inframammary incision.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".