Minimally Invasive Harvesting Technique for Costal Cartilage Graft: Donor Site, Morbidity and Aesthetic Outcomes
Bibliographic record
Abstract
Cartilage grafts are well-known as being reliable in reconstructive surgery for craniofacial pathologies. The aim of this study is to describe a new technique which requires an incision smaller than 1.5 cm but is still effective for harvesting cartilage graft. Thirty-six patients who underwent costal cartilage harvesting for septorhinoplasty have been included in this study, admitted from January 2018 to December 2021. Out of 36 patients, 34 have not reported any major complications, and two cases were followed up for pneumothorax. There were no infections and no chest wall deformities. All patients reported minimal pain at the donor site. The Vancouver Scar Scale was used to evaluate the entity of the postoperative scarring phenomena. This scale total ranges from 0 (representing normal skin) to a maximum score of 13 (representing worst scar imaginable). The results were 1.53 SD ± 0.64 (on average) 1 week after the surgical procedure and 1.28 SD ± 0.45 (on average) at the 6 months follow-up. This minimally invasive method provided a valid and effective surgical technique for cartilage graft. Despite the limitations of the case series, it seems that this procedure might be comparable to other and well-established traditional procedures and could be even preferred when the minimal invasiveness is mandatory.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".