Predicting Breast Reduction Weight Using the Mass of Breast Ptosis
Bibliographic record
Abstract
BACKGROUND: The preoperative prediction of therapeutic breast reduction weights, to achieve both relief of breast weight symptoms and yet achieve excellent breast shape, remains a challenge. OBJECTIVES: To design a simple clinical method to preoperatively predict and quantify therapeutic breast reduction weights. METHODS: In 31 women who underwent therapeutic bilateral reduction mammaplasty, the mass of the hypertrophic breast hanging below the inframammary fold was preoperatively weighed and then compared with the mass of the reduction specimen. Thirty patients underwent breast reduction using a superomedial nipple-areolar pedicle. Postoperative breast weight-related symptoms and breast shape findings were then noted. Statistical analysis relied on mean, SD, sample size, Mann-Whitney test for medians, Levene's test for variances and regression analysis. RESULTS: The average clinical follow-up was 160 days, with all patients achieving satisfactory breast size and shape from both the patient and surgeon's perspectives. All patients reported improvement of back pain, shoulder pain and lower neck pain. Two breasts developed delayed healing of the lateral skin flap, necessitating debridement and reclosure, followed by uneventful ongoing healing. There was no significant difference in preoperative ptotic breast mass and resectional breast mass (all P>0.05). CONCLUSIONS: Simple preoperative weighing of the ptotic portion of the hypertrophic breast can serve as a goal for the reduction weight, while creating pleasing breast proportions and improving breast weight-related symptoms. Preoperative quantification of the ptotic breast mass may guide the reduction technique and assist insurance precertification efforts.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".