Exploring Outcomes and Mediating Factors Following Supercharged End-to-Side Anterior Interosseous Nerve to Ulnar Nerve Transfer: A Scoping Review With Expert Insight
Bibliographic record
Abstract
Purpose: This scoping review with expert insight aims to map outcome measures following supercharged end-to-side anterior interosseous nerve to ulnar nerve transfer procedures, integrating clinical, patient-reported, and electrodiagnostic measures. It also explores surgical rationale and recovery trajectories, aiming to standardize methodologies and enhance patient care in nerve transfer surgeries. Methods: Our search encompassed multiple online databases, including MEDLINE, Embase, PubMed, and Google Scholar, ensuring rigor and comprehensiveness in identifying relevant literature. Results: Through scrutiny of 17 studies involving 300 patients from 300 articles, along with expert consultations on supercharged end-to-side nerve transfer for ulnar nerve entrapment, promising outcomes emerge, particularly in cubital tunnel syndrome. Primary measures such as Medical Research Council scale assessments and Disabilities of the Arm, Shoulder, and Hand scores demonstrate notable postsurgery improvements, with minor complications noted. Factors influencing recovery include preoperative dysfunction duration and surgical technique. Surgery indications prioritize high ulnar nerve injuries and severe cubital tunnel syndrome. Conclusions: The review highlights the importance of standardized outcome measures, early intervention, and comprehensive rehabilitation for optimizing supercharged end-to-side anterior interosseous nerve to ulnar nerve transfer outcomes. Type of study/level of evidence: Therapeutic IIIa.
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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 | 0.015 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".