Rat‐derived processed nerve allografts support more axon regeneration in rat than human‐derived processed nerve xenografts
Bibliographic record
Abstract
Processed nerve allografts are increasingly used as "off the shelf" nerve replacements for surgically bridging nerve gaps. Benchmarking the regenerative capacity of a commercially available human-derived nerve or xenograft in a rat nerve injury model would provide a convenient platform for future studies seeking to modify the processed nerve graft. Human and rat processed nerve grafts were used to bridge a 14 mm defect in a Sprague-Dawley rat sciatic nerve. Reversed autografts served as a positive control group. Twelve weeks following surgery, the distal nerve stumps were retrograde labeled and harvested for histology and histomorphometry. The cross-sectional areas of the human- and rat-derived processed nerve grafts were similar. Neuron counts and myelinated axon counts following use of the human-derived processed xenografts were decreased compared with those obtained from both the rat-derived processed nerve allografts and the autografts; the rat-derived processed nerve allografts were statistically equivalent to autografts. Measures of nerve fiber diameter and myelination revealed inferior axon regeneration maturity in both processed nerve grafts compared with autografts. Processed xenografts showed significantly reduced regeneration compared with autografts or processed allografts indicating that cross-species immunological reactions are important considerations in this rat model.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".