Use of the Upper Lateral Cartilage Sagittal Rotation Flap in Nasal Dorsum Reduction and Augmentation
Bibliographic record
Abstract
BACKGROUND: Medial rotation flaps of the upper lateral cartilages are useful in nasal dorsum reduction surgery because they maintain the separation between the upper lateral cartilages and the septum, and usually avoid the need for spreader grafts. These flaps, however, can be technically challenging. Sagittal rotation flaps of the upper lateral cartilages are more flexible and simpler to apply in nasal dorsum reduction surgery. These flaps can also be utilized in nasal dorsum augmentation surgery. METHODS: Seventy-one patients underwent dorsal reduction surgery utilizing a posteroinferior sagittal rotation of the upper lateral cartilages. In one-third of patients, in whom there was significant cartilage excess, the sagittal rotation was supplemented with a simplified and incremental medial rotation. Dorsal augmentation with anterosuperior sagittal rotation of the upper lateral cartilages was performed in 11 patients with select caudal dorsal deficiencies. RESULTS: The use of a sagittal rotation simplified the upper lateral cartilage flap procedure in dorsal reductions and significantly reduced the need for medial rotations. The technique was intuitive and could be applied to minor and major dorsal reductions. In select dorsal augmentations, the flap helped avoid the need for potentially visible onlay grafts. CONCLUSION: Sagittal rotation of the upper lateral cartilages helps preserve the normal anatomy of the upper laterals and the important relationship between the upper laterals and the septum.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".