3D printing strategies for peripheral nerve regeneration
Bibliographic record
Abstract
After many decades of biomaterials research for peripheral nerve regeneration, a clinical product (the nerve guide), is emerging as a proven alternative for relatively short injury gaps. This review identifies aspects where 3D printing can assist in improving long-distance nerve guide regeneration strategies. These include (1) 3D printing of the customizable nerve guides, (2) fabrication of scaffolds that fill nerve guides, (3) 3D bioprinting of cells within a matrix/bioink into the nerve guide lumen and the (4) establishment of growth factor gradients along the length a nerve guide. The improving resolution of 3D printing technologies will be an important factor for peripheral nerve regeneration, as fascicular-like guiding structures provide one path to improved nerve guidance. The capability of 3D printing to manufacture complex structures from patient data based on existing medical imaging technologies is an exciting aspect that could eventually be applied to treating peripheral nerve injury. Ultimately, the goal of 3D printing in peripheral nerve regeneration is the automated fabrication, potentially customized for the patient, of structures within the nerve guide that significantly outperform the nerve autograft over large gap injuries.
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".