Breast volume determination in breast hypertrophy: an accurate method using two anthropomorphic measurements.
Bibliographic record
Abstract
BACKGROUND: Precise determination of breast volume facilitates reconstructive procedures and helps in the planning of tissue removal for breast reduction surgery. Various methods currently used to measure breast size are limited by technical drawbacks and unreliable volume determinations. The purpose of this study was to develop a formula to predict breast volume based on straightforward anthropomorphic measurements. METHODS: One hundred one women participated in this study. Eleven anthropomorphic measurements were obtained on 202 breasts. Breast volumes were determined using a water displacement technique. Multiple stepwise linear regression was used to determine predictive variables and a unifying formula. RESULTS: Mean patient age was 37.7 years, with a mean body mass index of 31.8. Mean breast volumes on the right and left sides were 1328 and 1305 cc, respectively (range, 330 to 2600 cc). The final regression model incorporated the variables of breast base circumference in a standing position and a vertical measurement from the inframammary fold to a point representing the projection of the fold onto the anterior surface of the breast. The derived formula showed an adjusted R of 0.89, indicating that almost 90 percent of the variation in breast size was explained by the model. CONCLUSION: Surgeons may find this formula a practical and relatively accurate method of determining breast volume.
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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 | 0.000 | 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 | 0.000 | 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".