Application of Trans-Areola Approach for Costal Cartilage Harvest in Asian Rhinoplasty and Comparison with Traditional Approach on Donor-Site Morbidity
Bibliographic record
Abstract
BACKGROUND: The traditional approach of harvesting costal cartilage through a chest wall incision can result in significant donor-site morbidity and usually causes notable scars in Asian patients. This has become the main concern for Asian females seeking rhinoplasty with autologous costal cartilage. OBJECTIVES: The aim of this study was to investigate the donor-site morbidity of the trans-areola approach for costal cartilage harvest in Asian rhinoplasty and to compare it with the traditional approach. METHODS: Patients' records were reviewed to determine whether their rhinoplasties had been performed with either the trans-areola or the traditional approach to costal cartilage harvest. Donor-site morbidity was evaluated 1 year postoperatively via a visual analog scale and the Modified Vancouver Scar Scale. Long-term complications of the trans-areola group were assessed at least 6 months after surgery. RESULTS: There were 26 females in the trans-areola group and 35 females in the traditional group; both groups were of similar age and body mass index range. Compared with the traditional group, the trans-areola group had a significantly longer surgery time and a higher pneumothorax rate (7.7% vs 2.9%) but a significantly better scar quality and a higher overall satisfaction. Long-term outcomes and complications of the trans-areola group included significant scars (2/26, 7.7%), concavity of the breast (1/26, 3.8%), and local chest pain/discomfort (1/26, 3.8%). CONCLUSIONS: Compared with the traditional approach to harvesting costal cartilage in Asian rhinoplasty, patients who underwent the trans-areola approach had less overall donor-site morbidity and higher overall satisfaction. We recommend this technique to patients who meet the inclusion criteria as well as those seeking a better cosmetic outcome.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".