Reverse End-to-Side Nerve Transfer for Severe Ulnar Nerve Injury: A Western Canadian Multicentre Prospective Nonrandomized Cohort Study
Bibliographic record
Abstract
BACKGROUND: Reverse end-to-side (RETS) nerve transfer has become increasingly popular in patients with severe high ulnar nerve injury, but the reported outcomes have been inconsistent. OBJECTIVE: To evaluate the "babysitting effect," we compared outcomes after anterior interosseous nerve RETS transfer with nerve decompression alone. To evaluate the source of regenerating axons, a group with end-to-end (ETE) transfer was used for comparisons. METHODS: Electrophysiology measures were used to quantify the regeneration of anterior interosseous nerve (AIN) and ulnar nerve fibers while functional recovery was evaluated using key pinch and Semmes-Weinstein monofilaments. The subjects were followed postsurgically for 3 years. RESULTS: Sixty-two subjects (RETS = 25, ETE = 16, and decompression = 21) from 4 centers in Western Canada were enrolled. All subjects with severe ulnar nerve injury had nerve compression at the elbow except 10 in the ETE group had nerve laceration or traction injury. Postsurgically, no reinnervation from the AIN to the abductor digiti minimi muscles was seen in any of the RETS subjects. Although there was no significant improvement in compound muscle action potentials amplitudes and pressure detection thresholds in the decompression and RETS group, key pinch strength significantly improved in the RETS group ( P < .05). CONCLUSION: The results from published clinical trials are conflicting in part because crossover regeneration from the donor nerve has never been measured. Unlike those with ETE nerve transfers, we found that there was no crossover regeneration in the RETS group. The extent of reinnervation was also no different from decompression surgery alone. Based on these findings, the justifications for this surgical technique need to be carefully re-evaluated.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".