Perceptions of Ideal Breast and Areola Dimensions: A Survey Of 2,259 Respondents
Bibliographic record
Abstract
Introduction: Surgeons make decisions about areola size in breast procedures, but no formal study has been conducted to identify general views on ideal size. The objective of this study was to survey the global population to better understand their perceptions on ideal areola dimensions. Methods: The survey was created using SurveyMonkey and completed via Amazon Mechanical Turk over a 24-hour period. Participants’ demographics (sex, age group, country, state if located in the US, and race/ethnicity) were collected. Each participant had 9 composite diagrams of a female torso (combinations of 3 breast and 3 waist widths) and asked to select the best of 6 options with areola diameter: breast width ranging from 1:12 to 6:12. Results: Of 2,259 participants, with male(1,283;56.8%) and female(976;43.2%), majority were between 25 and 34 years old (1,012;44.8%), from US (1,669;73.9%), White race (1,430;63.3%), and had a Bachelor’s degree (1,426; 63.1%). Most participants selected 2:12 (32.89%) and second-most commonly selected 3:12 (30.61%)(P<0.0001). Males were more likely to choose extreme dimensions of 1:12 or 6:12 in comparison to females (P<0.0001). Across almost all races, 2:12 was most popular except among American Indian/Alaskan Native and Middle Eastern where 3:12 was most popular(P< 0.0001). Across the top 6 countries, US, India and Italy had 2:12 as the most popular and Brazil, Canada, and United Kingdom had 3:12 as the most preferred (P<0.0001). Conclusion: This study provides the first objective assessment of public’s impression of ideal areola proportions, and can guide surgical decision making in reconstructive procedures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".