Bibliographic record
Abstract
Introduction: Three-dimensional (3D) modeling and printing have become widely adopted in surgical fields, whether it be for pre-operative planning, production of prostheses, outcomes monitoring and even surgical training. Plastic and reconstructive surgeons have shown interest in using 3D technology in craniofacial reconstruction, in particular for microtia. Discussion: In patients with unilateral microtia, 3D modeling and printing of their normal contralateral ear to use as an intra-operative reference during costochondral or MedPor carving were preferred by surgeons to traditional 2D drawings as they provide the depth aspect of the ear and logistically save time. Combining tissue engineering with 3D modeling and printing by seeding chondrocytes onto a customized biodegradable ear framework is promising to restore aesthetics and obviates certain challenges of the autologous costochondral graft technique.Conclusions and relevance: Microtia is a common congenital malformation and its current gold standard is technically challenging. As medicine is moving towards personalized medicine, 3D modeling and printing will definitely play a larger role in various surgical fields, including microtia reconstruction. Future studies will likely focus on refining the acquisition of images to produce 3D models, standardizing tissue engineering techniques and using bioprinting to produce external ears once the technology is clinically applicable.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".