Radial Nerve Palsy: Nerve Transfer Versus Tendon Transfer to Restore Function
Bibliographic record
Abstract
BACKGROUND: Radial nerve injuries cause profound disability, and a variety of reconstruction options exist. This study aimed to compare outcomes of tendon transfers versus nerve transfers for the management of isolated radial nerve injuries. METHODS: A retrospective chart review of 30 patients with isolated radial nerve injuries treated with tendon transfers and 16 patients managed with nerve transfers was performed. Fifteen of the 16 patients treated with nerve transfer had concomitant pronator teres to extensor carpi radialis brevis tendon transfer for wrist extension. Preoperative and postoperative strength data, Disabilities of the Arm, Shoulder, and Hand (DASH) scores, and quality-of-life (QOL) scores were compared before and after surgery and compared between groups. RESULTS: For the nerve transfer group, patients were significantly younger, time from injury to surgery was significantly shorter, and follow-up time was significantly longer. Both groups demonstrated significant improvements in grip and pinch strength after surgery. Postoperative grip strength was significantly higher in the nerve transfer group. Postoperative pinch strength did not differ between groups. Similarly, both groups showed an improvement in DASH and QOL scores after surgery with no significant differences between the 2 groups. CONCLUSIONS: The nerve transfer group demonstrated greater grip strength, but both groups had improved pain, function, and satisfaction postoperatively. Patients who present early and can tolerate longer time to functional recovery would be optimal candidates for nerve transfers. Both tendon transfers and nerve transfers are good options for patients with radial nerve palsy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".