Alar Soft-Tissue Techniques in Rhinoplasty
Bibliographic record
Abstract
OBJECTIVES: To describe various techniques, including alar base reduction, alar flaring reduction, and alar hooding reduction and present a decision-making treatment algorithm and quantifiable guidelines for soft-tissue excision, along with scar outcomes from a single-surgeon practice. The soft tissue of the nasal tip, ala, and nostrils is important in overall nasal tip dynamics. Excisional alar contouring is an essential part of many successful cosmetic rhinoplasty outcomes. METHODS: The various soft-tissue excision techniques are described in detail and an algorithm is provided. Quantitative analysis of excision parameters was performed using statistical analysis. Finally, qualitative scar analysis was performed and scar outcomes were statistically derived. RESULTS: Seventy-four patients were female and 26 were male. Of the procedures reviewed, 47% involved alar soft-tissue excision. Alar base reduction was performed in 46 patients (46%). Alar flare reduction was performed in 16 patients (16%). Alar hooding reduction was performed in 2 patients (2%). Mean scar outcome scores ranged from 0.55 to 0.69. CONCLUSIONS: Alar soft-tissue techniques are often necessary to achieve a balanced outcome and superior results when performing rhinoplasty surgery. Therefore, they should be an integral part of every rhinoplasty evaluation and surgical plan as indicated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".