Early and Late Intrinsic Hand Muscle Reinnervation After End-to-Side AIN to Ulnar Motor Nerve Transfer
Bibliographic record
Abstract
Background: The “supercharge” end-to-side (SETS) anterior-interosseous-nerve (AIN) to ulnar-motor nerve transfer is used to improve intrinsic muscle recovery in cases of severe ulnar nerve compression or proximal axonotmetic injuries. Previous work has found differing intrinsic muscle recovery after this transfer. The objectives of this study were to examine the patterns of recovery in first dorsal interossei (FDI) and abductor digiti minimi (ADM) and the impact of AIN transfer to a specific fascicular location on the ulnar-motor nerve. Methods: A retrospective review of one fellowship-trained surgeon’s consecutive patients at a single center from December 2019 to September 2021 was conducted. Patients who had an AIN to ulnar-motor nerve transfer for any indication were included and were excluded if they had less than 9 months follow-up. Results: Seventeen patients were included (88% male, mean age 55 ± 14 years). At early follow-up, compound muscle action potential amplitudes for ADM and FDI did not increase. Compound muscle action potential amplitude for ADM significantly increased at late follow-up ( P < .01). Average British Medical Research Council (BMRC) strength increased at early follow-up for FDI ( P < .05), but not ADM. The proportion of patients with BMRC ≥ 3 increased for FDI ( P < .01) and ADM ( P < .05) at late follow-up. Volar-ulnar AIN insertion position did not have a clear effect on outcomes. Conclusions: The SETS AIN to ulnar-motor nerve transfer demonstrates clinical and electrophysiologic evidence of intrinsic muscle recovery and reinnervation, with differing recovery of outcomes. The role of specific fascicular targeting is still unclear and required further examination as does the mechanism behind differing intrinsic recovering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".