Bibliographic record
Abstract
BACKGROUND: The use of implants in aesthetic breast surgery may lead to complications resulting in the need for reoperation. This study examines outcomes following breast augmentation in a single surgeon's practice and investigates the effect of implant selection and surgical technique on complications and reoperations. METHODS: A retrospective review of a single surgeon's prospectively maintained database over 15 years was performed. All primary bilateral breast augmentation patients were included. Implant characteristics-including implant type, fill, shape, surface, and projection; incision type; and pocket location-were collected. Complications and reasons for reoperation were analyzed using survival analysis. RESULTS: One thousand five hundred thirty-nine patients with 3078 implants were included. Implant types included 596 shaped textured gel, 515 round smooth saline, 192 round textured gel, and 236 round smooth gel implants. Follow-up ranged from 0 to 155 months (average, 18 months). Total complication and reoperation rates were 6.8 and 7.7 percent, respectively. Inframammary incisions and the use of shaped textured gel implants were associated with lower rates of complications. The use of a dual-plane II or III pocket, and implant volumes over 400 cc, were associated with higher rates of complications. Full-projection round implants had rates of complications and reoperations equivalent to those of moderate-projection devices. Both textured shaped gel implants and a subpectoral pocket location were associated with the lowest rates of capsular contracture. CONCLUSION: This large series of breast implant patients demonstrates that both implant- and technique-related factors may influence complications and reoperations in breast implant surgery. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
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.
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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".