How Big Is Too Big? Exploring the Relationship between Breast Implant Volume and Postoperative Complication Rates in Primary Breast Augmentations
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 cm3 (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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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".