Nasal Tip Refinement: Analysis of Surgical Technique, Efficacy, and Secondary Effect of Skin Thickness
Bibliographic record
Abstract
BACKGROUND: Effective control and refinement of the nasal tip is an integral component of the rhinoplasty operation. A multitude of techniques exist to complement the rhinoplasty surgeon's approach, but attaining consistent and long-lasting results is still challenging due to the complex interplay between nasal anatomy and surgical technique. OBJECTIVES: The authors sought to determine whether the degree of tip narrowing with rhinoplasty is associated with the narrowing technique selected by the surgeon and whether there is an association between skin thickness and tip narrowing. METHODS: A retrospective chart review was conducted of all patients undergoing rhinoplasty in a single-surgeon practice between April 2004 and November 2006. Demographic information and specific operative techniques were recorded. Standard basal views of pre- and postoperative photographs were examined by two blinded observers, who measured nasal tip width relative to interpupillary distance with imaging software. Skin thickness was assessed and categorized by a surgical expert according to Fitzpatrick classification. RESULTS: Forty patients were identified with adequate follow-up and complete data sets. A statistically significant reduction in tip width pre- and postoperatively was seen (P = .041). No significant difference in tip narrowing between various techniques was found (P = .309), and no significant association between tip narrowing and skin thickness was identified (P = .186). CONCLUSIONS: Although tip-narrowing techniques are effective in rhinoplasty, the specific technique employed may not be clinically relevant. Furthermore, skin thickness may not be as significant a factor in tip narrowing as is commonly believed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".