Ideal Implant Structured Breast Implants: Core Study Results through 10 Years
Bibliographic record
Abstract
BACKGROUND: The Ideal Implant structured breast implant uses different technology than unstructured saline or silicone gel implants, making it a third type of implant. U.S. Food and Drug Administration (FDA) and Health Canada granted approval in November of 2014. This saline-filled implant has an internal structure consisting of a series of nested shells that support the upper pole when upright and control movement of the saline to provide a natural feel. Because women can look in the mirror to know their implants are intact, they have peace of mind. In contrast, most women are concerned about silicone gel implant ruptures, which are silent and require FDA-recommended magnetic resonance imaging or ultrasound scans for detection. METHODS: This U.S. trial enrolled 502 women: 399 for primary and 103 for revision augmentation. Investigators were 45 American Board of Plastic Surgery-certified plastic surgeons at 35 sites. Of the 502 women enrolled, 426 (84.9%) completed 10-year follow-up visits, a higher percentage than all other FDA breast implant trials. RESULTS: Through 10 years of follow-up, surgeon satisfaction was 94.8% for primary and 87.4% for revision augmentation; and patient satisfaction was 92.7% for primary and 82.3% for revision augmentation. Cumulative Kaplan-Meier risk rates for two major adverse events were lower than in the silicone gel implant trials: Baker class III and IV capsular contracture was 6.6% for primary and 11.5% for revision augmentation; and rupture/deflation was 3.7% for primary and 4.7% for revision augmentation. CONCLUSION: Ten-year results from 426 women show the Ideal Implant has high patient and surgeon satisfaction, a low rate of capsular contracture, and a low rate of rupture/deflation. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".