Traumatic Neuroma in Continuity Injury Model in Rodents
Bibliographic record
Abstract
Traumatic neuroma in continuity (NIC) results in profound neurological deficits, and its management poses the most challenging problem to peripheral nerve surgeons today. The absence of a clinically relevant experimental model continues to handicap our ability to investigate ways of better diagnosis and treatment for these disabling injuries. Various injury techniques were tested on Lewis rat sciatic nerves. Optimal experimental injuries that consistently resulted in NIC combined both intense focal compression and traction forces. Nerves were harvested at 0, 5, 13, 21, and 65 days for histological examination. Skilled locomotion and ground reaction force (GRF) analysis were performed up to 9 weeks on the experimental (n=6) and crush-control injuries (n=5). Focal widening, disruption of endoneurium and perineurium with aberrant intra- and extrafascicular axonal regeneration and progressive fibrosis was consistently demonstrated in 14 of 14 nerves with refined experimental injuries. At 8 weeks, experimental animals displayed a significantly greater slip ratio in both skilled locomotor assessments, compared to nerve crush animals (p<0.01). GRFs of the crush- injured animals showed earlier improvement compared to the experimental animals, whose overall GRF patterns failed to recover as well as the crush group. We have demonstrated histological features and poor functional recovery consistent with NIC formation in a rat model. The injury mechanism employed combines traction and compression forces akin to the physical forces at play in clinical nerve injuries. This model may serve as a tool to help diagnose this injury earlier and to develop intervention strategies to improve patient outcomes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".