Morselized Bone Graft: A Tool for Nasal Dorsum Refinement and Camouflaging
Bibliographic record
Abstract
Background: Refining the nasal dorsum to achieve a smooth and natural contour remains challenging, particularly in patients with thin skin who are prone to visible surface irregularities. Numerous techniques have been described to address these issues, including diced cartilage, fascial or dermal grafts, and synthetic implants. Objectives: This study evaluates the outcomes of using morselized bone grafts (MBG), specifically, autologous bone rasp material that is typically discarded, as a method for nasal dorsum contour refinement. Methods: A retrospective review was conducted of consecutive rhinoplasty procedures performed by the senior author between January 2021 and June 2022. Patients who underwent dorsal contouring with MBG and had at least 12 months of follow-up were included. The primary outcomes were postoperative infection and the need for revision surgery. Results: A total of 953 patients met inclusion criteria. The mean patient age was 31.6 ± 11.3 years, and the mean follow-up duration was 23.5 ± 8.7 months. Postoperative infections occurred in 26 patients (2.7%), all of which resolved with antibiotic therapy. Sixteen patients (1.7%) required operative revision. Conclusions: The use of MBG harvested from bone rasp material provides a safe and efficient option for achieving dorsal nasal smoothness and camouflaging minor contour irregularities in both primary and revision rhinoplasty. Additionally, MBG use is an efficient alternative to other techniques for addressing dorsal esthetics, specifically camouflaging minor irregularities, with no additional donor-site morbidity when paired with boney dorsal reduction.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".