Costal Cartilage Harvest Through a Small Incision in Asian Rhinoplasty After Breast Augmentation
Bibliographic record
Abstract
BACKGROUND: The costal cartilage is widely used in rhinoplasty. Although different surgical methods of costal cartilage harvest have been developed, few studies have reported the method of costal cartilage harvest for patients after breast augmentation. This study aims to provide our experience of costal cartilage harvest through a small incision approach. METHODS: A retrospective review was performed for patients undergoing esthetic rhinoplasty with costal cartilage after breast augmentation between May 2019 and May 2021. Postoperative pain was evaluated with the Visual Analog Scale, and the scars at the donor site were assessed 1 year postoperatively with the Modified Vancouver Scar Scale. RESULTS: A total of 23 female patients were included. The average follow-up time was 21.4 months. No complications of massive bleeding, pleural injury, or breast implant injury during the surgery, wound dehiscence, or wound infection in the harvested site were observed. No patients complained of changes in breast morphology or breast asymmetry after costal cartilage harvest. Results of Visual Analog Scale for donor-site pain indicated pain in donor-site peaked at 12 hours after surgery and gradually decreased. All patients were satisfied with the scarring of the donor sites after surgery. CONCLUSION: The better scar performance, low complication rates, and high satisfaction among patients suggest that this is a safe technique to harvest costal cartilage with a small incision in rhinoplasty for patients after breast augmentation.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 teacher head, 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".