Reoperation Rate After Primary Augmentation With Smooth, Textured, High Fill, Cohesive, Round Breast Implants (RANBI-I Study)
Bibliographic record
Abstract
BACKGROUND: Reoperation after primary breast augmentation remains an important clinical issue. OBJECTIVE: The authors sought to evaluate incidence and causes of reoperation in patients who underwent primary augmentation. METHODS: This retrospective, noninterventional study conducted at 16 Canadian sites reviewed medical records and patient-completed questionnaires of women who underwent primary breast augmentation with smooth or textured Natrelle Inspira implants containing TruForm 1 or TruForm 2 gel. Patients were aged ≥22 years, received implants via inframammary fold incision, and returned for follow-up at 2 to 4 years. RESULTS: A total of 319 women received Inspira implants (smooth TruForm 2, n = 205; textured TruForm 2, n = 99; smooth or textured TruForm 1, n = 15). At follow-up, 30 women (9.4%) had undergone reoperation, including 19 (9.3%) in the smooth TruForm 2 subgroup and 9 (9.1%) in the textured TruForm 2 subgroup. The mean time to reoperation was 1.2 years; the risk rate for reoperation was 9.9% at 3 years. The most common reasons for reoperation were implant malposition (36.7%), capsular contracture (33.3%), and the patient's request for a change in implant size or style (20.0%). Most women were very or somewhat satisfied with the initial surgery (89.3% overall; 90.7% smooth TruForm 2; 86.9% textured TruForm 2). Thirty-four women (10.7%) reported adverse events, including 20 (9.8%) in the smooth TruForm 2 subgroup and 14 (14.1%) in the textured TruForm 2 subgroup. CONCLUSIONS: This analysis suggests that Natrelle Inspira TruForm 2 implants are safe when used in primary breast augmentation, resulting in low reoperation rates that are consistent with those for other breast implants.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 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".