Cartilage Chips in Rhinoplasty
Bibliographic record
Abstract
OBJECTIVE: This study reveals that the cartilage chips can be a good solution for camouflage and augmentation in rhinoplasty. METHODS: In this study, 64 patients who had undergone rhinoplasty from 2014 to 2019 were retrospectively studied. The average age was 31. Forty-nine patients had primary and 15 revision rhinoplasties. Cartilage chips were cut into less than 0.5-mm thickness dimensions changing from 2 to 10 mm. They were used to fill deep radix, depressions at the key area, supratip area, around the grafts to prevent their visibility at the tip. In addition, they were used in the fascia for augmentation. The cartilage chips were sculpted from the septal cartilage in 47, rib in 16, and ear cartilage in one case. RESULTS: They were applied on the radix in 25, middle vault in 37, supratip area in 32, and on the tip in 12 cases. In 30 cases, cartilage chips were mixed with cartilage dust for better fixation and camouflage. They were placed in the fascia in three cases for dorsal augmentation. Complications were seen in three cases in the form of irregularities. CONCLUSION: Cartilage chips are found to be a powerful solution in terms of camouflage and augmentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".