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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".