Preferred Nasolabial Angle in Rhinoplasty: A Cross-Sectional Analysis
Bibliographic record
Abstract
BACKGROUND: The perception of an ideal nose is influenced by a variety of factors, with demographic characteristics playing a significant role in what is considered an ideal nose. The nasolabial angle (NLA) is considered one of the defining features shaping the nose. OBJECTIVES: In this study we set out to capture the perception of the ideal nasolabial angle among Canadian, Saudi, Kuwaiti, and Lebanese populations. METHODS: An online questionnaire-based cross-sectional study was conducted to investigate the ideal nasolabial angle among Canadian, Saudi, Kuwaiti, and Lebanese populations (n = 197). Participants were patients attending outpatient clinics, plastic surgery residents, and medical students. The questionnaire included demographics and the perception of respondents of the ideal NLA for each gender: male (85°, 90°, 95°, 100°, 110°) and females (95°, 100°, 110°, 115°). RESULTS: The majority of respondents were female (81.2%), ages between 20 and 39 (84.3%). The mean and standard deviation of ideal NLA choices in both male and female models were 97.1 ± 6.39 and 109.5 ± 5.32, respectively. The ideal male NLA choices were found to correlate significantly with age (P = .044) and work status (P = .019). In choosing the ideal female NLA, age was a significant factor (P = .012). CONCLUSIONS: Identifying the ideal NLA is essential to establishing aesthetic goals for patient and surgeon alike. It is important to understand the effects of demographics on the choice of the ideal NLA, which ultimately influences the planning and outcome of the rhinoplasty procedure.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".