Peripheral nerve repair throughout the body with processed nerve allografts: Results from a large multicenter study
Bibliographic record
Abstract
BACKGROUND: Peripheral nerve damage resulting in pain, loss of sensation, or motor function may necessitate a reconstruction with a bridging material. The RANGER® Registry was designed to evaluate outcomes following nerve repair with processed nerve allograft (Avance® Nerve Graft; Axogen; Alachua, FL). Here we report on the results from the largest peripheral nerve registry to-date. METHODS: This multicenter IRB-approved registry study collected data from patients repaired with processed nerve allograft (PNA). Sites followed their own standard of care for patient treatment and follow-up. Data were assessed for meaningful recovery, defined as ≥S3/M3 to remain consistent with previously published results, and comparisons were made to reference literature. RESULTS: The study included 385 subjects and 624 nerve repairs. Overall, 82% meaningful recovery (MR) was achieved across sensory, mixed, and motor nerve repairs up to gaps of 70 mm. No related adverse events were reported. There were no significant differences in MR across the nerve type, age, time-to-repair, and smoking status subgroups in the upper extremity (p > .05). Significant differences were noted by the mechanism of injury subgroups between complex injures (74%) as compared to lacerations (85%) or neuroma resections (94%) (p = .03) and by gap length between the <15 mm and 50-70 mm gap subgroups, 91 and 69% MR, respectively (p = .01). Results were comparable to historical literature for nerve autograft and exceed that of conduit. CONCLUSIONS: These findings provide clinical evidence to support the continued use of PNA up to 70 mm in sensory, mixed and motor nerve repair throughout the body and across a broad patient population.
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.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".