The rule of thirds: Determining the ideal areolar proportions
Bibliographic record
Abstract
BACKGROUND: Breast surgery often requires changing the diameter of the areola. Recommended areolar size is commonly based on population averages, or surgical judgement. An ideal areola size has not been previously been described. We hypothesized that the ideal areolar diameter would be proportional to two breast measurements not commonly altered during breast surgery: the nipple diameter and breast base width. METHODS: 'The Sun' newspaper (London, UK) publishes photographs of topless models which are selected based on the aesthetic appeal of their non-operated breasts. The publication's archive, from March 2014 to January 2017, was independently reviewed by three authors to identify photographs that presented a clear anterior view of the breast. The base width, nipple diameter and areolar diameter were measured independently by each reviewer. Measurements were pooled, and the mean was included for analysis. Ratios of the areolar diameter to the base width and the nipple diameter were calculated. RESULTS: The photographs of 58 models were eligible for inclusion. The average areolar diameter to base width was 0.29 (SD = 0.05). The average nipple to areolar diameter was 0.29 (SD = 0.06). CONCLUSIONS: In aesthetically pleasing breasts, the areolar diameter is proportional to both the breast base width and nipple diameter. Breast base width is commonly measured preoperatively in aesthetic breast procedures, and is not typically modified. Breast base width can therefore be used to determine the ideal areolar size using the ratio of areola:base width ratio of 0.29 identified in this study.
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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 | 0.014 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".