Twelve Steps to Optimize Scar Outcomes in External Rhinoplasty
Bibliographic record
Abstract
A well-healed scar extends beyond a mere superficial concern, reflecting the precision and care underlying the surgery. This study addresses the lack of recent literature on the complete closure technique for external rhinoplasty scars, emphasizing its potential impact on patient satisfaction and overall aesthetic quality. The main purpose of this article was to introduce and assess the efficacy of a novel closing suture technique for external rhinoplasty and emphasize on 12 specific steps for wound closure. A cohort of 104 patients who underwent external rhinoplasty utilizing the proposed closing suture technique were evaluated. Standardized photographic documentation and scar assessment were performed using the Vancouver-Manchester scale, considering notching, skin level differences, pigmentation, and overall appearance. The surgical approach involved a combination of vertical mattress and interrupted simple sutures, creating an effective closure framework. Analysis revealed an absence of poor scars, with 83.6% classified as good. Note that 65.5% of the good scars achieved a perfect 6-point score. While one moderate scar scored 3 points, the majority demonstrated optimal outcomes. Clinical photographs further illustrated the technique's success, showcasing imperceptible scars and reinforcing the efficacy of the complete closure approach. Our study introduces and assesses the efficacy of the complete closure technique for external rhinoplasty scars. The approach consistently yields positive outcomes, with an emphasis on almost imperceptible scars. While not achieving a 100% imperceptible scar rate, this study underscores the need for ongoing research to refine techniques and enhance patient satisfaction in external rhinoplasty. The proposed technique contributes significantly to scar management practices and encourages further exploration within the evolving landscape of rhinoplasty.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".