Spreader graft placement in endonasal rhinoplasty: Technique and a review of 100 cases
Bibliographic record
Abstract
BACKGROUND: Spreader grafts are widely considered to be the mainstay of treatment for insufficient internal nasal valve and are commonly placed preventively during rhinoplasty, after hump removal, to avoid middle vault collapse. Although the placement and suturing of spreader grafts in open rhinoplasty is fairly easy, their positioning and stabilization in endonasal rhinoplasty is associated with a learning curve. METHODS: A review of the technique with tips for the novice surgeon is presented, particularly as pertains to correct placement. The technique can be used to insert spreader grafts irrespective of whether the nasal dorsum is addressed. Suturing is usually unnecessary. A retrospective review of 100 patients in whom spreader grafts were placed was undertaken to evaluate complications such as poor placement, displacement or other complications. RESULTS: Although there is a learning curve to ensure the dorsal mucosal attachment is maintained while developing the pocket sufficiently dorsally for proper graft placement, the technique is easy to learn, effective, quick and technically simple to perform. Of 100 patients, three had a cartilaginous dorsal spur as the cephalic edge of the graft became visible. One patient developed an ecchymosis along the dorsum that caused a hump that resolved in two months. There were no other aesthetic or functional complications. CONCLUSION: The endonasal placement technique provides for simple, safe and easy placement, as well as stabilization of spreader grafts during endonasal rhinoplasty, with few complications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".