Relationship Between Conflict of Interest and Reported Outcomes After Upper Extremity Nerve Reconstruction Using Acellular Nerve Allografts: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Acellular nerve allografts (ANAs) are increasingly used for peripheral nerve repair. However, the effect of industry sponsorship on reported outcomes remains unclear. This review evaluates functional outcomes following upper extremity nerve reconstruction with ANAs, stratified by conflict of interest (COI). METHODS: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses-guided search of MEDLINE, PubMed, and Embase identified clinical studies using ANAs. Data extracted included study design, patient age, nerve gap length, outcome measures (Medical Research Council Classification, Disabilities of the Arm, Shoulder, and Hand, visual analog scale), and COI status. Outcomes were compared using independent t -tests. RESULTS: Twenty-eight studies met inclusion criteria. Non-COI studies involved older patients and longer nerve gaps. Motor recovery was markedly higher in COI studies compared with non-COI studies (69.8% vs. 14.1%; P < 0.001), whereas sensory recovery also differed markedly (25.9% vs. 80.4%; P < 0.001). DISCUSSION: These findings suggest that outcome reporting may be influenced by funding source. The use of subjective measures and study design limitations further complicate objective interpretation. CONCLUSION: Although ANAs offer promise, current evidence is shaped by sponsorship bias. Future research should prioritize standardized, objective assessments, and independent, prospective studies to guide clinical decision making.
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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 | 0.017 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 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".