When Reductive Rhinoplasty Goes Wrong and How to Make it Right
Bibliographic record
Abstract
Over-resection leads to many complications in rhinoplasty. Here, we detail the most common of these pitfalls and strategies to both avoid and repair them.The nose is frequently divided into thirds. The nasal bones represent the upper third. The middle third is composed of the dorsal septum and upper lateral cartilages. The lower third is comprised of the lower lateral cartilages and tip-supporting structures.The commonly seen sequelae of over-resection include a deep radix, saddle nose deformity, inverted-V deformity, pollybeak deformity, alar retraction, a pinched nasal tip, bossae, deep alar grooves, and external nasal valve collapse. The major mechanism to avoid these issues is avoidance; however, several grafting techniques are described here to correct overly aggressive reduction.It is critical to avoid the complications described in this manuscript. Just as important, it is necessary to know how to correct these deformities when patients present for revision 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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".