Breast Augmentation: A Geographical Comparison
Bibliographic record
Abstract
OBJECTIVE: To describe and compare physical characteristics and implant details of women undergoing primary cosmetic breast augmentation in different geographical locations. METHODS: Three cohorts of 100 consecutive breast augmentation cases in university settings were retrospectively reviewed for patient demographic and implant information in Kelowna (British Columbia), Loma Linda (California, USA) and Temple (Texas, USA). Statistical analysis was performed with a Kruskal-Wallis test without normality assumption (P<0.05 was considered to be significant). Pearson correlation coefficients were also determined for body mass index (BMI) versus implant volume at each of the sites. RESULTS: The three group medians were significantly different for weight, BMI and implant volume. Kelowna's average patient was 33 years of age, had a BMI of 20.8 kg/m(2) and an implant volume of 389 mL. Loma Linda's average patient was 32 years of age, had a BMI of 21.6 kg/m(2) and an implant volume of 385 mL. Temple's average patient was 36 years of age, had a BMI of 22.6 kg/m(2) and an implant volume of 335 mL. Pearson correlations for BMI versus implant volume were statistically significant in the Loma Linda and Temple groups. CONCLUSION: Patients from different geographical locations undergoing breast augmentation were similar in age, height and parity, but varied in weight, BMI and implant volume. A positive linear correlation between BMI and implant volume was found in the American cohorts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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 teacher head, 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".