Current Surgical Outcomes of Nasal Tip Grafts in Rhinoplasty: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Refinement of the nasal tip plays an important role in rhinoplasty surgery outcomes and may be considered the most technically challenging aspect of the procedure. Numerous techniques have been described for nasal tip augmentation utilizing grafts. The aim of this study was to systematically review the existing literature on nasal tip grafts and appraise it critically. METHODS: A PubMed search was performed to identify journal articles related to nasal tip grafts from the past decade. A total of 44 studies met inclusion criteria. The Newcastle-Ottawa Quality Assessment Scale and Jadad scale were used to appraise 38 observational studies and six randomized trials, respectively, to determine the quality of the studies. RESULTS: Critical assessment revealed that the studies were highly variable in focus and encompassed autologous, homologous, and alloplastic grafts. The quality of the data included an average Newcastle-Ottawa Quality Assessment Scale score of 6.5 (out of 9) and Jadad score of 2.5 (out of 5). A majority of studies (86.4%) included objective outcomes using anthropometric measurements and a portion of studies (27.3%) also included patient-reported outcomes. CONCLUSIONS: The results of this systematic review suggest that more than one type of nasal tip graft may result in satisfactory outcomes. This review provides an expansive collection of studies on nasal tip grafts, which can serve as an invaluable tool for the plastic surgeon engaging in rhinoplasty.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".