Single-Stage Breast Reconstruction Using an All-In-One Adjustable Expander/Implant
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: When tissue expansion is necessary in breast reconstruction, a single-stage approach is possible using adjustable expander/implants, with or without the use of acellular dermal matrix. We aimed to present the senior author’s single-stage experience over a period of 12 years using combined expander/implants in breast reconstruction. Methods: This is a Single-institution, retrospective review of breast reconstruction with combined expander/implants from 2002 to 2014. Logistic regression was performed to evaluate the impact of multiple variables on long-term outcomes. Results: A total of 162 implants in 105 patients were included in this study. Mean follow-up time was 81.7 months (SD, ± 39.2; range, 15–151). Complication rates were as follows: 0.62% extrusion, 1.2% mastectomy flap necrosis, 1.2% hematoma, 1.9% dehiscence, 2.5% seroma, 4.9% infection, and 15.4% deflation. The following associations were identified by logistic regression: adjuvant radiotherapy and capsular contracture ( P = 0.034), tumor size and deflation ( P = 0014), and smoking history and infection ( P = 0.013). Conclusions: Overall, 81% of breasts were successfully reconstructed in a single stage. Single-stage reconstruction using all-in-one expander/implants reduces costs by eliminating the need for a second procedure under general anesthesia and can achieve results comparable with other alloplastic reconstructions reported in the literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it