Supercharge End-to-Side Anterior Interosseous–to–Ulnar Motor Nerve Transfer Restores Intrinsic Function in Cubital Tunnel Syndrome
Bibliographic record
Abstract
BACKGROUND: The supercharge end-to-side anterior interosseous nerve-to-ulnar motor nerve transfer offers a viable option to enhance recovery of intrinsic function following ulnar nerve injury. However, in the setting of chronic ulnar nerve compression where the timing of onset of axonal loss is unclear, there is a deficit in the literature on outcomes after supercharge end-to-side anterior interosseous nerve-to-ulnar motor nerve transfer. METHODS: A retrospective study of patients who underwent supercharge end-to-side anterior interosseous nerve-to-ulnar motor nerve transfer for severe cubital tunnel syndrome over a 5-year period was performed. The primary outcomes were improvement in first dorsal interosseous Medical Research Council grade at final follow-up and time to reinnervation. Change in key pinch strength; grip strength; and Disabilities of the Arm, Shoulder and Hand questionnaire scores were also evaluated using paired t tests and Wilcoxon signed rank tests. RESULTS: Forty-two patients with severe cubital tunnel syndrome were included in this study. Other than age, there were no significant clinical or diagnostic variables that were predictive of failure. There was no threshold of compound muscle action potential amplitude below which supercharge end-to-side anterior interosseous nerve-to-ulnar motor nerve transfer was unsuccessful. CONCLUSIONS: This study provides the first cohort of outcomes following supercharge end-to-side anterior interosseous nerve-to-ulnar motor nerve transfer in chronic ulnar compression neuropathy alone and underscores the importance of appropriate patient selection. Prospective cohort studies and randomized controlled trials with standardized outcome measures are required. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".