Impact of Body Mass Index and Comorbidities on Outcomes in Upper Extremity Nerve Transfers
Bibliographic record
Abstract
Abstract Background There is a paucity of research investigating the impact of patient comorbidities, such as obesity and smoking, on nerve transfer outcomes. The objective of this retrospective cohort study was to evaluate the impact of body mass index (BMI) and comorbidities on the clinical outcomes of upper extremity nerve transfers. Methods A retrospective cohort study was executed. Patients were eligible for inclusion if they had an upper extremity nerve transfer with a minimum of 12-months follow-up. Data was collected regarding demographics, comorbidities, injury etiology, nerve transfer, as well as preoperative and postoperative clinical assessments. The primary outcome measure was strength of the recipient nerve innervated musculature. Statistical analysis used the Mann-Whitney U test, Wilcoxon signed-rank test, and Spearman's rho. Results Thirty-eight patients undergoing 43 nerve transfers were eligible for inclusion. Patients had a mean age of 48.8 years and a mean BMI of 27.4 kg/m2 (range:19.7–39.0). Injuries involved the brachial plexus (32%) or its terminal branches (68%) with the most common etiologies including trauma (50%) and compression (26%). Anterior interosseous nerve to ulnar motor nerve (35%) was the most common transfer performed. With a mean follow-up of 20.1 months, increased BMI (p = 0.036) and smoking (p = 0.021) were associated with worse postoperative strength. Conclusion This retrospective cohort study demonstrated that increased BMI and smoking may be associated with worse outcomes in upper extremity nerve transfers—review of the literature yields ambiguity in both regards. To facilitate appropriate patient selection and guide expectations regarding prognosis, further experimental and clinical work is warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".