Does skin thickness affect satisfaction post rhinoplasty?
Bibliographic record
Abstract
OBJECTIVES: To determine the mean nasal skin thickness in the Middle Eastern population and to assess the effect of skin thickness on patients' satisfaction following rhinoplasty surgeries. Methods: Radiological measurements of skin thickness at the 3 vertical thirds of the nasal dorsum were taken. A total of 154 patients (80 females and 74 males) who were scheduled for computed tomography scan for the paranasal sinuses were included in the study. The patients were then categorized into 3 groups: thick, medium, and thin nasal skin. A scale from 10% to 100% was used to assess patient satisfaction following rhinoplasty. Satisfaction and skin thickness were analyzed using the Kruskal-Wallis test. Results: Nasal skin thickness for males was 6.13, 2.76 millimeter (mm) from the upper and 3.70 mm to the lower third. For females, it was 5.34, 2.13 mm from the upper and 3.21 mm to the lower third. There was no statistically significant difference in patient satisfaction among the 3 skin thickness groups (p=0.089). Conclusion: This study provides baseline results of nasal skin thickness in the Middle Eastern population. The results also show that nasal skin thickness may not be a strong factor affecting patient satisfaction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".