Neurogenic motor evoked potentials: role in brachial plexus surgery
Bibliographic record
Abstract
Peripheral nerve graft repair after severe brachial plexus injury is futile if there is degeneration of motor fibers in the proximal nerve stump to which the graft must be attached. Traditional intraoperative neurophysiological assessment methods like nerve action potential (NAP) and somatosensory evoked potential (SSEP) monitoring have been used to evaluate proximal nerve stump integrity, but these methods do not allow evaluation of the integrity of motor fibers back to the anterior horn cell. Consequently, the authors used transcranial electrical stimulation and recorded neurogenic motor evoked potentials (MEPs) directly from the brachial plexus in a patient undergoing surgical repair of a complete upper brachial plexus injury (Erb palsy) to assess the functional continuity of motor fibers. In addition, selected elements of the brachial plexus were directly stimulated, and NAPs were recorded. Finally, SSEPs were recorded from the scalp after stimulation of selected elements of the brachial plexus. Neurogenic MEPs were present from the medial cord of the brachial plexus, but not the middle or upper trunk; NAPs were present from the lateral and posterior cords after middle trunk stimulation, but absent after upper trunk stimulation; and SSEPs were present after medial cord stimulation but absent after stimulation of the upper and middle trunks. For the first time, neurogenic MEPs were coupled with NAPs and SSEPs to evaluate successfully the functional status of motor fibers back to the anterior horn cell for accurate localization of the lesion sites.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".