Anterior Interosseous–to–Ulnar Motor Nerve Transfers: A Single Center’s Experience in Restoring Intrinsic Hand Function
Bibliographic record
Abstract
Background: Transfer of the anterior interosseous nerve (AIN) into the ulnar motor branch improves intrinsic hand function in patients with high ulnar nerve injuries. We report our outcomes of this nerve transfer and hypothesize that any improvement in intrinsic hand function is beneficial to patients. Methods: A retrospective review of all AIN-to-ulnar motor nerve transfers, including both supercharged end-to-side (SETS) and end-to-end (ETE) transfers, from 2011 to 2018 performed by 2 surgeons was conducted. All adult patients who underwent this nerve transfer for any reason with greater than 6 months’ follow-up and completed charts were included. Primary outcome measures were motor function using the British Medical Research Council (BMRC) grading system and subjective satisfaction with surgery using a visual analog scale. Secondary outcome measures included complications and donor site deficits. Results: Of the 57 patients who underwent nerve transfer, 32 patients met the inclusion criteria. The average follow-up and average time to surgery were 12 and 15.6 months, respectively. The overall average BMRC score was 2.9/5, with a trend toward better recovery in patients who received earlier surgery (<12 months = BMRC 3.7, ≥12 months = BMRC 2.2; P < .01). Patients with an SETS transfer had better results that those with an ETE transfer (SETS = 3.2, ETE = 2.6). There were no donor deficits after operation. One patient developed complex regional pain syndrome. Conclusions: Patients with earlier surgery and an in-continuity nerve (receiving an SETS transfer) showed improved recovery with a higher BMRC grade compared with those who underwent later surgery. Any improvements in intrinsic hand function would be beneficial to patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".