Mastoid Fascia Tissue Graft as a Tip Camouflage Technique in Rhinoplasty: A Reliable Alternative to Soft Cartilage Grafts
Bibliographic record
Abstract
BACKGROUND: Traditional rhinoplasty tip grafts often lead to visibility issues, prompting patients to seek revision surgery. The mastoid fascia tissue graft (MFTG) provides a natural-appearing alternative with an acceptable risk of complication. The MFTG remains less visible through the skin and helps camouflage and conceal tip irregularities. This study of 193 patients examines the MFTG's effectiveness in nasal tip refinement, evaluating revision and infection rates. METHODS: A retrospective analysis of MFTG use for nasal tip appearance during open rhinoplasty in the senior author's (R.G.R.) practice was conducted, covering the period from January of 2019 to June of 2022. Inclusion criteria encompassed open rhinoplasty cases using mastoid tissue for tip appearance with at least 12 months of follow-up. Among 2003 cases, 193 met these criteria and were evaluated for subsequent revision and infection rates. RESULTS: The average patient age was 34.2 years (175 female patients and 18 male patients). Primary rhinoplasty was performed in 113 patients, with 80 receiving revision operations. The average follow-up was 14.8 months. Six patients (3.1%) overall needed extended antibiotics, including 1 primary rhinoplasty patient (0.9%) and 5 secondary rhinoplasty patients (6.3%). Overall, revision rhinoplasty was required in 6 patients (3.1%) (1 primary patient [0.9%] and 5 secondary rhinoplasty patients [6.3%]). CONCLUSIONS: MFTG use for an aesthetically pleasing nasal tip appearance is a safe, convenient, and effective technique for camouflaging and concealing nasal tip contour irregularities in both primary and revision rhinoplasty. Use of the MTFG is associated with minimal morbidity. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".