Optimal Technique for Cutting Peripheral Nerves in Nerve Transfer Surgery: A Survey of Peripheral Nerve Surgeons
Bibliographic record
Abstract
Background: Nerve transfer procedures are performed in patients with proximal nerve injuries to optimize their potential for functional recovery. The study aimed to determine the preferred surgical technique and tool used by peripheral nerve surgeons to transect nerves in nerve transfers. Methods: All current members of the American Society of Peripheral Nerve were invited to complete a cross-sectional 10-question survey. Data on practice demographics, nerve-cutting instruments/techniques used, and their belief on whether this impacted patient outcomes were collected. Results: A total of 49 American Society of Peripheral Nerve members participated in the study, the majority of whom were over 10 years into practice (n = 30/49; 61%). The most common response was a scalpel blade (n = 26/49; 53%), with the remaining 47% using iris scissors, micro-serrated scissors, a razor blade, specialized nerve microscissors, or a specialized nerve-cutting device. The number of years in practice (P = 0.0271) and the percentage of practice that involves treating patients with peripheral nerve injuries (P = 0.0054) is significantly associated with the belief that crushing the donor nerves during transection may result in worse outcomes following nerve transfer. Only the latter is significantly associated with this belief in recipient nerves (P = 0.0214). Conclusions: Our findings demonstrate that peripheral nerve surgeons believe that the technique used to transect nerves before coaptation influences outcomes after nerve transfer. Further ex vivo studies are necessary to investigate how different cutting techniques influence nerve morphology and scarring at the coaptation site to optimize outcomes after peripheral nerve surgery.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".