Canadian Society for Aesthetic (Cosmetic) Plastic Surgery/Société canadienne de chirurgie plastique esthétique
Bibliographic record
Abstract
Implant selectIon In breast augmentatIonDc Hammond grand rapids, michigan One of the most critical variables related to successful breast augmentation is implant selection.Despite the numerous choices with regard to implant design, and the many ways such variables including shape, texture and fill can be mixed and matched, there are very consistent and well-defined principles that can be identified to enhance the aesthetic success of the procedure.Such principles include maximizing soft tissue cover, merging volume with skin elasticity to enhance a harmonious relationship with the native breast, matching implant dimensions to the patient's anatomy, and precisely controlling the boundaries of the breast pocket.In particular, respecting the anatomy of the inframammary fold can greatly affect the overall result.By combining intelligent and strategic manipulation of these variables, and coupling them with artistic technical expertise, optimal results in breast augmentation can be achieved. comparatIve outcomes of smootH versus textureD rounD gel Implants In prImary breast augmentatIonKm Davidge, r levine, mH brown toronto, ontario PurPose: Textured gel implants have several potential advantages over smooth devices including lower rates of capsular contracture, reduced implant mobility, and less long-term stretch of lower pole tissues.Disadvantages of textured implants include greater potential for contamination during insertion, increased seroma rate, and traction rippling.No prior direct comparison of textured versus smooth gel implants has been made.This study sought to compare the experience and clinical outcomes associated with textured versus smooth round gel implants in primary breast augmentation.Methods: A consecutive series of patients undergoing primary bilateral breast augmentation with round silicone gel implants (2004)(2005)(2006)(2007)(2008)(2009) were identified from two breast augmentation practices.Patients receiving textured versus smooth implants were compared on clinical, operative, and outcome (complications, reoperations) characteristics.A stratified comparison of complications and reoperations by implant pocket was also performed.Parametric (Student's t test, chi square test) and nonparametric tests were utilized for statistical comparisons, as appropriate.results: 336 patients (mean age 32.0 years; mean f/u 8.2 months) were included.Patients receiving smooth (n=165) and textured (n=171) round gel implants were similar with respect to demographics and indications for augmentation.Moderate profile implants were most common in both study groups, but median implant size was slightly smaller in the textured group (300 vs. 325, p=0.003).Textured devices were more frequently placed in a subpectoral pocket (63.7% vs. 40.0%,p<0.0001), and through an inframammary fold incision (87.1% vs. 77.6%,p=0.053).Total number of complications (p=0.004) and reoperations (p=0.017) were lower in patients with textured versus smooth implants.Patients receiving textured implants had fewer capsular contractures than patients receiving smooth implants when implants were placed in the subglandular position (5.0% vs. 14.7%, p=0.059).This effect was lost for implants placed in a subpectoral pocket.ConClusions: In this series, complications and reoperations were less frequent with textured implants.Our findings support existing evidence that textured implants are associated with decreased capsular contracture rates, and that this benefit exists mainly for subglandular augmentation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.092 | 0.014 |
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".