Mastopexy for breast ptosis: Utility outcomes of population preferences
Bibliographic record
Abstract
Background Breast ptosis can occur with aging, and after weight loss and breastfeeding. Mastopexy is a procedure used to modify the size, contour and elevation of sagging breasts without changing breast volume. To gain more knowledge on the health burden of living with breast ptosis requiring mastectomy, validated measures can be used to compare it with other health states. Objective To quantify the health state utility assessment of individuals living with breast ptosis who could benefit from a mastopexy procedure; and to determine whether utility scores vary according to participant demographics. Methods Utility assessments using a visual analogue scale (VAS), time trade-off (TTO) and standard gamble (SG) methods were used to obtain utility scores for breast ptosis, monocular blindness and binocular blindness from a sample of the general population and medical students. Linear regression and the Student's t test were used for statistical analysis; P<0.05 was considered to be statistically significant. Results Mean (± SD) measures for breast ptosis in the 107 volunteers (VAS: 0.80±0.14; TTO: 0.87±0.18; SG: 0.90±0.14) were significantly different (P<0.0001) from the corresponding measures for monocular blindness and binocular blindness. When compared with a sample of the general population, having a medical education demonstrated a statistically significant difference in being less likely to trade years of life and less likely to gamble risk of a procedure such as a mastopexy. Race and sex were not statistically significant independent predictors of risk acceptance. Discussion For the first time, the burden of living with breast ptosis requiring surgical intervention was determined using validated metrics (ie, VAS, TTO and SG). The health burden of living with breast ptosis was found to be comparable with that of breast hypertrophy, unilateral mastectomy, bilateral mastectomy, and cleft lip and palate. Furthermore, breast ptosis was considered to be closer to ‘perfect health’ than monocular blindness, binocular blindness, facial disfigurement requiring face transplantation surgery, unilateral facial paralysis and severe lower extremity lymphedema. Conclusions Quantifying the health burden of living with breast ptosis requiring mastopexy indicated that is comparable with other breast-related conditions (breast hypertrophy and bilateral mastectomy). Numerical values have been assigned to this health state (VAS: 0.80±0.14; TTO: 0.87±0.18; and SG: 0.90±0.14), which can be used to form comparisons with the health burden of living with other disease states.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".