A Geometric Analysis of Mastectomy Incisions: Optimizing Intraoperative Breast Volume
Bibliographic record
Abstract
INTRODUCTION: The advent of acellular dermis-based tissue expander breast reconstruction has placed an increased emphasis on optimizing intraoperative volume. Because skin preservation is a critical determinant of intraoperative volume expansion, a mathematical model was developed to capture the influence of incision dimension on subsequent tissue expander volumes. METHODS: A mathematical equation was developed to calculate breast volume via integration of a geometrically modelled breast cross-section. The equation calculates volume changes associated with excised skin during the mastectomy incision by reducing the arc length of the cross-section. The degree of volume loss is subsequently calculated based on excision dimensions ranging from 35 mm to 60 mm. RESULTS: A quadratic relationship between breast volume and the vertical dimension of the mastectomy incision exists, such that incrementally larger incisions lead to a disproportionally greater amount of volume loss. The vertical dimension of the mastectomy incision - more so than the horizontal dimension - is of critical importance to maintain breast volume. Moreover, the predicted volume loss is more profound in smaller breasts and primarily occurs in areas that affect breast projection on ptosis. CONCLUSIONS: The present study is the first to model the relationship between the vertical dimensions of the mastectomy incision and subsequent volume loss. These geometric principles will aid in optimizing intra-operative volume expansion during expander-based breast reconstruction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.002 |
| 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) | 0.002 | 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".