3D printing strategies for peripheral nerve regeneration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 0.000 |
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 it