Developing a Consensus-Based Core Set of Outcome Measures for Ulnar Nerve Surgery: A Delphi Study With Focus on the Supercharged End-to-Side Anterior Interosseous Nerve to Ulnar Nerve Transfer
Bibliographic record
Abstract
Purpose: The supercharged end-to-side (SETS) anterior interosseous to ulnar nerve transfer is increasingly used to augment intrinsic muscle recovery in patients with severe ulnar neuropathy. However, there is a lack of standardized outcome measures to evaluate the effectiveness of this procedure. This study aimed to develop a consensus-based core set of outcome measures applicable to ulnar nerve surgery, with a specific focus on the SETS transfer. Methods: A two-round modified Delphi process was conducted involving 15 multidisciplinary experts in hand surgery and upper limb rehabilitation. The initial survey was informed by a comprehensive literature review and expert opinion. Participants ranked outcome domains and corresponding measurement tools based on their relevance in both clinical and research settings. Consensus was defined as ≥75% agreement. A second-round survey was conducted to refine the results and focus specifically on outcomes applicable to SETS procedures. Results: In Round 1, experts reached consensus on several key domains including motor function, functional ability, quality of life, pain, dexterity, and sensory evaluation. In Round 2, a refined core outcome set for SETS procedures was established. Lateral pinch strength and daily living function self-assessment were prioritized across both settings. Validated tools endorsed included the Patient-Rated Ulnar Nerve Evaluation, Short Form-12, visual analog scale, nine-hole peg test, and two-point discrimination. Conclusions: This Delphi study established a consensus-based core outcome set for evaluating surgical outcomes following ulnar nerve reconstruction, with specific application to SETS procedures. The proposed framework integrates patient-reported and clinician-rated measures and may support standardization in future research and clinical practice. Clinical relevance: The lack of standardized outcomes for ulnar nerve transfers limits cross-study comparisons and evidence synthesis. This study provides a structured outcome set to support consistent evaluation and improve decision making in patients undergoing SETS or related procedures.
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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 | 0.247 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".