Outcomes of the Use of Fresh-Frozen Costal Cartilage in Rhinoplasty
Bibliographic record
Abstract
BACKGROUND: Rhinoplasty is made more challenging when there is insufficient septal cartilage for use as graft material. Several autologous and homologous graft options have been used in the past, although each comes with its own set of challenges. Fresh-frozen costal cartilage (FFCC) is an increasingly popular alternative that yields the benefits of homologous tissue while having a lower theoretical risk profile. Given the relatively novel nature of this option, the authors analyzed the complication rates of Musculoskeletal Transplant Foundation FFCC. METHODS: A retrospective chart review of the use of FFCC in rhinoplasty in the senior author's (R.G.R.) practice was conducted between March of 2018 to December of 2021. A total of 282 cases were reviewed and analyzed for rates of infection, warping, and resorption. Patients with a minimum of 12 months of follow-up were included. RESULTS: The mean age of the study group was 35.8 years, and 27 male and 255 female patients were included. Forty cases were primary rhinoplasties; the remaining 242 were revisions. The mean follow-up period was 20.3 months. Six patients (2.1%) required empiric antibiotics postoperatively; no patient had clinical signs of warping, resorption, or displacement, and 6 patients (2.1%) required operative revision unrelated to the FFCC. CONCLUSIONS: This study provides follow-up data on the complication profile of FFCC in rhinoplasty. Acute infection, warping, and resorption rates were found to be no greater than rhinoplasty complication rates when autologous or homologous tissue is used. FFCC is a safe, convenient, and patient-centered option for graft tissue in rhinoplasty. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".