Impact of Surgery Timing on Outcomes After Nerve Transfer to Restore Elbow Flexion
Bibliographic record
Abstract
Background: Nerve reconstruction following brachial plexus injury (BPI) is a time-sensitive procedure, and surgical delay may negatively impact muscle reinnervation and outcomes. This study investigated the impact of surgical timing on elbow flexion strength in patients with BPI undergoing nerve transfer to restore elbow flexion. Methods: Following PRISMA guidelines, MEDLINE, Embase, and the Cochrane Library databases were systematically searched. English-language studies investigating the single fascicular transfer (SFT) or double fascicular transfer (DFT) to restore elbow flexion in BPI were included. Data were analyzed to identify the predictors of elbow flexion strength: surgery timing, age, injury level, and SFT versus DFT. Results: The literature search identified 1051 articles. Studies (n = 31) reporting data of individual patients who underwent SFT (n = 341) or DFT (n = 67) were included; the mean age was 29.6 ± 11.2 years, time from injury to surgery was 6.5 ± 5.0 months, and follow-up was 27.1 ± 24.3 months. Good elbow flexion strength was found: Medical Research Council grade greater than or equal to 3 in 352 (86.3%) and Medical Research Council grade greater than or equal to 4 in 288 (70.6%). In the adjusted analysis, poorer motor recovery was associated with increased age (P = 0.02), surgical delay (P < 0.0001), C5-7 injuries (P < 0.01), and pan-plexus injuries (P < 0.0001). A 32% reduction in the odds of favorable motor recovery was observed with a 3-month delay to surgery. Patients who had a nerve transfer 6 months or earlier from injury had 2.4 times the odds of favorable motor recovery (P < 0.001). Conclusions: SFT and DFT provide excellent elbow flexion strength in the majority of patients. Following nerve transfers in individuals with BPI, poorer motor recovery was observed with each 3-month delay to surgery.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".