Clinical Applications of Three-Dimensional Photography in Breast Surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Three-dimensional imaging in breast surgery has several uses clinically. The most practical applications are for the evaluation of breast asymmetries, both congenital and acquired, and for the evaluation of factors affecting breast shape in augmentation mammaplasty. Other uses of three-dimensional imaging that we have found clinically helpful are for evaluation of patients desiring reduction mammaplasty and for evaluation of patients undergoing unilateral breast reconstruction to determine the expander and permanent implant size that gives the best symmetry with the contralateral breast. We present five cases in which we investigate the use of three-dimensional imaging clinically by using the images to determine quantitative information about the breast, such as volume or projection. Overall, three-dimensional imaging is very helpful in providing objective information about the breast for use in preoperative planning. In addition, by analyzing clinical cases, it can provide objective data about the breast and surgical mammaplasty (especially augmentation mammaplasty) that may help surgeons better understand those factors that contribute to breast shape and influence surgical outcomes. There are currently some limitations of this system, influenced by patients with significant ptosis or obesity, which may introduce errors into the three-dimensional data, making them unreliable. However, we believe three-dimensional imaging has great clinical potential in surgical mammaplasty.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it