Patient-Reported Measurement of Breast Asymmetry Using Archimedes’ Principle in Breast Reduction Mammaplasty: A Retrospective Study
Bibliographic record
Abstract
Introduction Breast hypertrophy is a common condition that is often treated with breast reduction surgery. A large percentage of breast hypertrophy patients have notable asymmetry between breasts. Methods The purpose of this study was to investigate a method of measuring breast asymmetry, one that allows patients to determine the asymmetry of their own breasts at home with ease, and to assess its accuracy and role in a surgical practice. A retrospective chart review was conducted, wherein self-measurements of breast asymmetry using a variation of Bouman's technique were compared with the recorded intra-operative resected tissue mass. Results In total, 47 patients with asymmetry were included in the study. The difference between patient-reported measurements and resected breast tissue mass varied from 0 grams to 240 grams. Of the 47 patients, 38% were able to measure their breast difference within a remarkable 10 grams as compared to the resected breast tissue, of which four patients were accurate to less than one gram. The majority (70%) of patients accurately measured their asymmetry within 50 grams, which was determined to be a clinically significant amount based on a survey of plastic surgeons performed for the study. Conclusion The breast measurement technique presented in this study appears to be effective and accurate for most patients with suspected asymmetry undergoing reduction mammaplasty that stands to reduce pre-operative planning time. Patient-reported breast measurement may emerge as a valuable tool in clinical and research pursuits; however, further research on this topic is indicated at this time.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".