Nerve Transfer or Musculotendinous Transfer for Elbow Flexion: What Is the Evidence? A Systematic Review
Bibliographic record
Abstract
Both nerve transfers and tendon transfers can restore elbow flexion after upper extremity nerve injury. Our objective was to determine whether there is any difference in outcomes in patients undergoing a nerve transfer versus tendon transfer to restore elbow flexion between 6 and 12 months following injury. Two independent reviewers reviewed electronic databases to identify studies that reported patients undergoing a nerve or tendon transfer within 6−12 months after injury and evaluated any of the following outcomes: range of motion, strength, quality of life, cost, or complications. Methodological quality of the studies was assessed using the MINORS scale. Seven of 417 retrieved articles met the inclusion criteria and were included in the final analysis. The interrater agreement was high (κ = 0.88). The mean range of motion was 21.7 degrees for 3 nerve transfer patients compared with 122.5 degrees for 2 tendon transfer patients. Of 5 nerve transfer patients, mean postoperative strength was 2.4 kg compared with 3.5 kg for 2 tendon transfer patients. There is consistent low-quality evidence to support the effectiveness of tendon transfer with respect to range of motion and strength in patients with elbow injury. A meta-analysis was not feasible because of the heterogeneity in the study designs and outcomes. There is a strong need for future longitudinal randomized trials to make definitive conclusions regarding the effectiveness of tendon transfers over nerve transfers.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".