Technical progress and clinical application of distal nerve transfers.

Introduction: Distal nerve transfer surgery has emerged as a transformative strategy in the treatment of peripheral nerve injuries. However, substantial heterogeneity in injury patterns, donor selection, surgical techniques, and outcome measures has produced conflicting evidence and limited consensus regarding optimal reconstructive approaches. To address these limitations, we propose a concept of four-level decision-making framework integrated with a donor feasibility triad consisting of availability, suitability, and acceptability. Methods: This paper integrates the author's surgical experience with current literature and conceptualizes the decision-making process through the proposed framework. No formal systematic review or original data collection was performed. Three testable predictions were derived and assessed using evidence from systematic reviews, meta-analyses, and large clinical series. Results: The reviewed literature demonstrated substantial heterogeneity and frequent contradictions regarding donor selection, reconstructive strategies, and technical approaches. There was also substantial heterogeneity in the evaluation of the three predictions, as they were primarily qualitative constructs without standardized quantitative thresholds or uniform assessment criteria. Prediction 1, proposing a worse outcome prognosis with an increase in lesion severity, was consistently supported. Prediction 2, proposing superior outcomes with feasibility triad-compliant donor selection, remains insufficiently evaluated. Prediction 3, proposing that technical modifications and adjunctive modalities cannot fully compensate for poor donor feasibility, was supported. Discussion: The framework does not prescribe a fixed algorithm but provides a structured language to articulate why a donor choice was made, what constraints existed, and where uncertainties lie, enabling meaningful comparison across heterogeneous clinical scenarios. Prospective validation of the predictions and standardized outcome reporting are needed to improve interpretability and reproducibility across studies.