Template for Diced Cartilage with Platelet-Rich Fibrin (PRF) in Rhinoplasty: An Easy Solution for Millimetric Camouflage of the Full Dorsal Esthetic Unit
Bibliographic record
Abstract
Dorsal irregularities are one of the most common issues in modern rhinoplasty. Rhinoplasty surgeons propose placing interface grafts lining the hole dorsum to ensure a natural unoperated look. Diced cartilage embedded in Choukroun's platelet-rich fibrin (PRF) scaffold being one of the most recent innovations in the field. However, no method has been described to help with the creation of thin, malleable, and reproductive graft with millimetric precision using that technique. The senior author details his protocol and his experience with a newly developed template for the creation of reproductive grafts with standard size and thickness, using diced cartilage, injectable PRF (iPRF), and advanced PRF (aPRF), for full dorsal camouflage and lining in rhinoplasty. This retrospective case series was conducted to evaluate the results looking at the dorsal esthetic unit at a minimum of 6 months for patients who beneficiated from a millimetric dorsal augmentation from 1 to 2mm using diced cartilage with iPRF and aPRF. The author reports his experience with 54 cases operated from April 2018 to May 2022 using his newly developed template.The template is an innovative technique to allow faster and more reliable fabrication of soft grafts using diced cartilage with both iPRF and aPRF. This novel approach allows for millimetric dorsal augmentation from 1 to 2mm with great precision and high reproductivity with good esthetic outcome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".