3-Dimensional Simulation for Breast Augmentation: Does the Software Actually Work?
Bibliographic record
Abstract
BACKGROUND: Recent advances in 3-dimensional (3D) imaging, such as the Vectra XT 3D imaging system (Vectra; Canfield Imaging Systems), have enabled simulations for breast augmentation. These simulations are popular and valuable in preoperative consultations, but no studies have evaluated the system's predictive volume accuracy with a large sample size or explored the patient-specific and surgical factors influencing these simulations. METHODS: A prospective cohort study was conducted on patients undergoing breast augmentation simulations with Vectra between January of 2021 and December of 2022. Predictive volumetric accuracy was analyzed with a ±10% precision threshold and Bland-Altman visualization. Linear breast measurements were compared using mean and median absolute error analysis, and a generalized linear model with generalized estimating equations was used to identify factors affecting volume prediction accuracy. RESULTS: The study included 78 patients (154 breasts). Accurate predictions (±10% error) were achieved in 73% of cases, with an average margin of error of 7.52%. The most accurate measurement was sternal-nipple ( P = 0.64) and the least accurate measurement was nipple-inframammary fold ( P = 0.001). Factors influencing prediction accuracy included pregnancy history, preoperative breast volume, and implant volume. CONCLUSIONS: Vectra accurately predicts postoperative breast volumes within a 10% margin of error, although precision decreases with larger breasts (greater than 650 cc). Patient-specific factors, such as pregnancy history and preoperative breast characteristics, should guide preoperative consultations. The system enhances patient education, shared decision-making, and participation in care, reinforcing its value in preoperative planning for breast augmentation.
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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 | 0.022 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".