Normative Data for Interpreting the BREAST-Q: Augmentation
Bibliographic record
Abstract
BACKGROUND: The BREAST-Q is a rigorously developed, well-validated, patient-reported outcome instrument with a module designed for evaluating breast augmentation outcomes. However, there are no published normative BREAST-Q scores, limiting interpretation. METHODS: Normative data were generated for the BREAST-Q Augmentation module by means of the Army of Women, an online community of women (with and without breast cancer) engaged in breast-cancer related research. Members were recruited by means of e-mail; women aged 18 years or older without a history of breast cancer or breast surgery were invited to participate. Descriptive statistics and a linear multivariate regression were performed. A separate analysis compared normative scores to findings from previously published BREAST-Q augmentation studies. RESULTS: The preoperative BREAST-Q Augmentation module was completed by 1211 women. Mean age was 54 ± 24 years, the mean body mass index was 27 ± 6 kg/m, and 39 percent (n = 467) had a bra cup size of D or greater. Mean scores were as follows: Satisfaction with Breasts, 54 ± 19; Psychosocial Well-being, 66 ± 20; Sexual Well-being, 49 ± 20; and Physical Well-being, 86 ± 15. Women with a body mass index of 30 kg/m or greater and bra cup size of D or greater had lower scores. In comparison with Army of Women scores, published BREAST-Q augmentation scores were lower before and higher after surgery for all scales except Physical Well-being. CONCLUSIONS: The Army of Women normative data represent breast-related satisfaction and well-being in women not actively seeking breast augmentation. These data may be used as normative comparison values for those seeking and undergoing surgery as we did, demonstrating the value of breast augmentation in this patient population.
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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 | 0.028 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".