A new method to quantify innervation of the ulnar intrinsic hand muscles by the anterior interosseous nerve in <scp>Martin‐Gruber</scp> anastomosis and nerve transfer surgery
Bibliographic record
Abstract
INTRODUCTION/AIMS: It is important to quantify the amount of crossover innervation from the anterior interosseous nerve (AIN) through Martin-Gruber anastomosis (MGA) particularly in patients with high ulnar nerve injury who undergo nerve transfer surgery. The objective of this study is to describe a novel electrophysiological method for quantifying innervation from the AIN that can be done using conventional nerve conduction study setup and commonly available software for analysis. METHODS: Seven subjects with MGA and nine patients who had undergone AIN to ulnar nerve transfer underwent conventional motor nerve conduction studies. Recording was done over the hypothenar and first dorsal interosseous muscles while stimulating the median and ulnar nerves at the wrist and elbow. Datapoint-by-datapoint subtraction of the compound muscle action potentials evoked at the elbow and wrist was performed after they had been onset-aligned. The results were compared to the collision technique and innervation ratio method. RESULT: Results from the digital subtraction method were highly correlated with the collision technique (r = 0.96, p < 0.05). In contrast, its correlation with the innervation ratio method is substantially lower. DISCUSSION: In comparison to previously described techniques, the digital subtraction method has a number of practical advantages. It uses conventional nerve conduction study setup, and the added step of digital alignment and subtraction can be done through commonly available software. With the increasing use of nerve transfer surgery in severe high ulnar nerve injury, this could be a useful method to identify the presence of MGA prior to surgery and for evaluating nerve recovery following 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".