How Big Is Too Big? Exploring the Relationship between Breast Implant Volume and Postoperative Complication Rates in Primary Breast Augmentations
Why this work is in the frame
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Bibliographic record
Abstract
Background: There is no consensus regarding implant size as an independent risk factor for complications in primary breast augmentation. Choosing appropriate implant volume is an integral part of the preoperative planning process. The current study aims to assess the relationship between implant size and the development of complications following augmentation mammaplasty. Methods: A retrospective chart review of patients undergoing primary breast augmentation at the Westmount Institute of Plastic Surgery between January 2000 and December 2021 was conducted. Demographics, implant characteristics, surgical technique, postoperative complications, and follow-up times were recorded. Univariate logistic regression was used to identify independent predictors, which were then included in multivariate logistic regressions of implant volume and implant volume/body mass index (BMI) ratio regarding complications. Results: A total of 1017 patients (2034 breasts) were included in this study. The average implant volume used was 321.4 ± 57.5 cm 3 (range: 110–605). Increased volume and volume/BMI ratio were associated with a significant increase in risk of implant rupture (odds ratio = 1.012, P < 0.001 and 1.282, P < 0.001 respectively). Rates of asymmetry were significantly associated with increases in implant volume and volume/BMI ratio (odds ratio = 1.005, P = 0.004 and 1.151, P < 0.001, respectively). No single implant volume or volume/BMI ratio above which risks of complications significantly increase was identified. Conclusions: Implant rupture and postoperative asymmetries are positively correlated with bigger implant volumes. Implant size could likely be a useful independent predictor of certain complications, especially in patients with high implant to BMI ratios.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it