Role of electrodiagnosis in nerve transfers for focal neuropathies and brachial plexopathies
Bibliographic record
Abstract
Over the past 2 decades, the surgical treatment of brachial plexus and peripheral nerve injuries has advanced considerably. Nerve transfers have become an important surgical tool in addition to nerve repair and grafting. Electrodiagnosis has traditionally played a role in the diagnosis and localization of peripheral nervous system injuries, but a different approach is needed for surgical decision-making and monitoring recovery. When patients have complete or severe injuries they should be referred to surgical colleagues early after injury, as outcomes are best when nerve transfers are performed within the first 3 to 6 mo after onset. Patients with minimal recovery of voluntary activity are particularly challenging, and the presence of a few motor unit action potentials in these individuals should be interpreted on the basis of timing and evidence of ongoing reinnervation. Evaluation of potential recipient and donor muscles, as well as redundant muscles, for nerve transfers requires an individualized approach to optimize the chances of a successful surgical intervention. Anomalous innervation takes on new importance in these patients. Communication between surgeons and electrodiagnostic medicine specialists (EMSs) is best facilitated by a joint collaborative clinic. Ongoing monitoring of recovery post-operatively is critical to allow for decision making for continued surgical and rehabilitation treatments. Different electrodiagnostic findings are expected with resolution of neurapraxia, distal axon sprouting, and axonal regrowth. As new surgical techniques become available, EMSs will play an important role in the assessment and treatment of these patients with severe nerve injuries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".