Tissue Engineering Nasal Cartilage Grafts with Three-Dimensional Printing: A Comprehensive Review
Bibliographic record
Abstract
Nasal cartilage serves a crucial structural function for the nose, where rebuilding the cartilaginous framework is an essential aspect of nasal reconstruction. Conventional methods of nasal reconstruction rely on autologous cartilage harvested from patients, which contributes to donor site pain and the potential for site-specific complications. Some patients are not ideal candidates for this procedure due to a lack of adequate substitute cartilage due to age-related calcification, differences in tissue quality, or due to prior surgeries. Tissue engineering, combined with three-dimensional printing technologies, has emerged as a promising method of generating biomimetic tissues to circumvent these issues to restore normal function and aesthetics. We conducted a comprehensive literature review to examine the applications of three-dimensional printing in conjunction with tissue engineering for the generation of nasal cartilage grafts. This review aims to compare various approaches and discuss critical considerations in the design of these grafts. Impact Statement The main goal of this review is to summarize the current state of tissue engineering applications that use three-dimensional printing technologies to create nasal cartilage grafts, while providing contextual background on the principles of nasal reconstruction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".