Changing Role of Septal Extension versus Columellar Grafts in Modern Rhinoplasty
Bibliographic record
Abstract
Effective control of nasal tip projection and rotation is a key component in modern rhinoplasty. Tip projection is a product of several anatomical factors: length and strength of lower lateral cartilages, the suspensory ligament, fibrous connections to the upper lateral cartilages, and the anterior septal angle. Several cartilage grafts have been described for effectively altering nasal tip projection and rotation. Columellar struts and septal extension grafts are both commonly used in modern rhinoplasty to affect projection and rotation of the nasal tip. Although columellar strut grafts have shown moderate efficacy in maintaining tip projection and unifying the tip complex, their effect on increasing tip projection has been shown to be very limited. In comparison, septal extension grafts have been shown to effectively control tip projection, rotation, and shape by securing the nasal tip to the septum. Varieties of septal extension grafts have been described to support the medial crura and control tip shape, all of which depend on the presence of a stable caudal septum. The type of graft used is dependent on the specific characteristics of the underlying tip structures. The authors' aim is to provide an updated classification of cartilage grafts for altering nasal tip projection and rotation, and an algorithmic approach for their implementation. Although both columellar struts and septal extension grafts offer the modern rhinoplasty surgeon a way to alter tip projection and rotation, they do vary in efficacy. Understanding which graft to use and in what setting is key in successfully controlling projection, rotation, and shape of the nasal tip.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".