Three-Dimensional Imaging and Breast Measurements: How Predictable Are We?
Bibliographic record
Abstract
BACKGROUND: Outcomes in aesthetic breast surgery are dependent on preoperative breast measurements. The accuracy of 3-dimensional (3D) imaging in measuring critical landmarks in augmentation mammaplasty surgery has not been described. OBJECTIVES: We aimed to determine the predictability of 3D imaging compared to direct measurements. METHODS: Two raters measured the breasts of 28 women using four anthropometric (direct) measurements: sternal notch to nipple distance (Sn-N), nipple to midline (N-M), nipple to inframammary-fold distance under maximal stretch (N-IMF), and base width (BW). Measurements (indirect) were also obtained using 3D imaging. Statistical analysis was completed with Bland-Altman plots. RESULTS: Each rater collected 56 data points for each of the four measurements. This resulted in 224 data points per rater. The Sn-N measurement had a 0.05 cm (SD, 0.65) difference in the mean values obtained between direct and indirect measurements. N-M had a mean difference of 0.20 cm (SD, 0.62). The mean difference for BW was 1.26 cm (SD, 0.69 cm), and N-IMF showed a mean difference of 1.22 cm (SD, 0.74 cm). Three-dimensional imaging overestimated Sn-N, N-M, and BW, while it underestimated N-IMF. CONCLUSIONS: Three-dimensional imaging has good utility and is most accurate for Sn-N and N-M measurements, which require frontal imaging of a standing patient. BW and N-IMF are less accurate due to obscured landmarks on frontal imaging. The medial and lateral aspects of the breast may be obscured when measuring BW on 3D imaging, which may explain this difference. N-IMF is a dynamic measurement, and as a result, 3D imaging has limited ability to measure this distance accurately.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".