Assessment, patient selection, and rehabilitation of nerve transfers
Bibliographic record
Abstract
Peripheral nerve injuries are common and can have a devastating effect on physical, psychological, and socioeconomic wellbeing. Peripheral nerve transfers have become the standard of care for many types of peripheral nerve injury due to their superior outcomes relative to conventional techniques. As the indications for, and use of, nerve transfers expand, the importance of pre-operative assessment and post-operative optimization increases. There are two principal advantages of nerve transfers: (1) their ability to shorten the time to reinnervation of muscles undergoing denervation because of peripheral nerve injury; and (2) their specificity in ensuring proximal motor and sensory axons are directed towards appropriate motor and sensory targets. Compared to conventional nerve grafting, nerve transfers offer opportunities to reinnervate muscles affected by cervical spinal cord injury and to augment natural reinnervation potential for very proximal injuries. This article provides a narrative review of the current scientific knowledge and clinical understanding of nerve transfers including peripheral nerve injury assessment and pre- and post-operative electrodiagnostic testing, adjuvant therapies, and post-operative rehabilitation for optimizing nerve transfer 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".