Bioengineering for head and neck reconstruction: the role of customized flaps
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this review is to provide the reader with an overview of the present and future applications of bioengineering for head and neck reconstruction, ranging from the application of Computed Assisted Surgery (CAS) to the most recent advances in 3D printing and tissue engineering. RECENT FINDINGS: The use of CAS in head and neck reconstruction has been demonstrated to provide shorter surgical times, improved reconstructive accuracy of bone reconstruction, and achieves better alignment of bone segments in osteotomized reconstructions. Beyond its classical application in bone reconstructions, CAS has demonstrated reliability in the planning and harvesting of soft tissue flaps. To date, literature regarding bioengineering for head and neck reconstruction is mainly focused on in-vitro and animal model experiments; however, some pioneering reports on human patients suggest the potential feasibility of this technology. SUMMARY: Bioengineering is anticipated to play a key role in the future development of customized flaps for head and neck reconstruction. These technologies are particularly appealing as a new technology to address certain unsolved challenges in head and neck 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 | 0.002 | 0.002 |
| 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) | 0.004 | 0.002 |
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".